mathlea_10 <- get_data("EDFacts_math_achievement_lea_2010_2019")

rlalea_10 <- get_data("EDFacts_rla_achievement_lea_2010_2019")

fiscal2010 <- get_data("NCES_CCD_fiscal_district_2010")

Data Visualization 1

Rebecca

Research Question:

How do high school students’ subgroup makeup (i.e., Race/ethnicity, Male vs. Female, economically disadvantaged, Limited English, Migrant status, Disability status, and Homelessness) differ among states/regions?

Datasets:

  • EDFacts_math_achievement_lea_2010_2019
  • EDFacts_rla_achievement_lea_2010_2019
  • EDFacts_rla_participation_lea_2013_2019
  • EDFacts_math_participation_lea_2013_2019
skim(mathlea_10)
Data summary
Name mathlea_10
Number of rows 15747
Number of columns 232
_______________________
Column type frequency:
character 118
numeric 113
POSIXct 1
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
ALL_MTH00PCTPROF 0 1.00 1 5 0 134 0
ALL_MTH03PCTPROF 1367 0.91 2 5 0 114 0
ALL_MTH04PCTPROF 1368 0.91 2 5 0 122 0
ALL_MTH05PCTPROF 1356 0.91 2 5 0 122 0
ALL_MTH06PCTPROF 1354 0.91 1 5 0 123 0
ALL_MTH07PCTPROF 1779 0.89 1 5 0 125 0
ALL_MTH08PCTPROF 1836 0.88 1 5 0 129 0
ALL_MTHHSPCTPROF 3666 0.77 1 5 0 131 0
CWD_MTH00PCTPROF 189 0.99 1 5 0 127 0
CWD_MTH03PCTPROF 2014 0.87 2 5 0 105 0
CWD_MTH04PCTPROF 2002 0.87 1 5 0 111 0
CWD_MTH05PCTPROF 2006 0.87 1 5 0 116 0
CWD_MTH06PCTPROF 2031 0.87 1 5 0 108 0
CWD_MTH07PCTPROF 2384 0.85 1 5 0 108 0
CWD_MTH08PCTPROF 2463 0.84 1 5 0 114 0
CWD_MTHHSPCTPROF 4155 0.74 1 5 0 108 0
ECD_MTH00PCTPROF 405 0.97 1 5 0 129 0
ECD_MTH03PCTPROF 1792 0.89 2 5 0 116 0
ECD_MTH04PCTPROF 1776 0.89 2 5 0 120 0
ECD_MTH05PCTPROF 1754 0.89 1 5 0 123 0
ECD_MTH06PCTPROF 1772 0.89 1 5 0 123 0
ECD_MTH07PCTPROF 2169 0.86 1 5 0 124 0
ECD_MTH08PCTPROF 2215 0.86 1 5 0 128 0
ECD_MTHHSPCTPROF 3952 0.75 1 5 0 130 0
FIPST 0 1.00 2 2 0 51 0
F_MTH00PCTPROF 47 1.00 1 5 0 125 0
F_MTH03PCTPROF 1459 0.91 2 5 0 105 0
F_MTH04PCTPROF 1467 0.91 2 5 0 114 0
F_MTH05PCTPROF 1463 0.91 2 5 0 115 0
F_MTH06PCTPROF 1460 0.91 1 5 0 117 0
F_MTH07PCTPROF 1877 0.88 1 5 0 119 0
F_MTH08PCTPROF 1933 0.88 1 5 0 127 0
F_MTHHSPCTPROF 3743 0.76 1 5 0 125 0
FILEURL 0 1.00 88 88 0 1 0
HOM_MTH00PCTPROF 9382 0.40 2 5 0 95 0
HOM_MTH03PCTPROF 10803 0.31 2 5 0 40 0
HOM_MTH04PCTPROF 10855 0.31 2 5 0 39 0
HOM_MTH05PCTPROF 10855 0.31 2 5 0 41 0
HOM_MTH06PCTPROF 10923 0.31 1 5 0 40 0
HOM_MTH07PCTPROF 11042 0.30 1 5 0 39 0
HOM_MTH08PCTPROF 11069 0.30 1 5 0 40 0
HOM_MTHHSPCTPROF 11704 0.26 1 5 0 46 0
LEAID 0 1.00 7 7 0 15747 0
LEANM09 0 1.00 3 60 0 15465 0
LEP_MTH00PCTPROF 4294 0.73 1 5 0 125 0
LEP_MTH03PCTPROF 6354 0.60 2 5 0 110 0
LEP_MTH04PCTPROF 6505 0.59 1 5 0 112 0
LEP_MTH05PCTPROF 6738 0.57 1 5 0 116 0
LEP_MTH06PCTPROF 6940 0.56 1 5 0 103 0
LEP_MTH07PCTPROF 7267 0.54 1 5 0 104 0
LEP_MTH08PCTPROF 7406 0.53 1 5 0 102 0
LEP_MTHHSPCTPROF 8633 0.45 1 5 0 85 0
MAM_MTH00PCTPROF 4167 0.74 2 5 0 99 0
MAM_MTH03PCTPROF 8056 0.49 2 5 0 43 0
MAM_MTH04PCTPROF 8050 0.49 2 5 0 45 0
MAM_MTH05PCTPROF 8049 0.49 2 5 0 43 0
MAM_MTH06PCTPROF 7977 0.49 2 5 0 41 0
MAM_MTH07PCTPROF 8141 0.48 2 5 0 41 0
MAM_MTH08PCTPROF 8129 0.48 2 5 0 40 0
MAM_MTHHSPCTPROF 8926 0.43 2 5 0 43 0
MAS_MTH00PCTPROF 3309 0.79 2 5 0 92 0
MAS_MTH03PCTPROF 6732 0.57 2 5 0 61 0
MAS_MTH04PCTPROF 6711 0.57 2 5 0 63 0
MAS_MTH05PCTPROF 6747 0.57 2 5 0 68 0
MAS_MTH06PCTPROF 6816 0.57 2 5 0 70 0
MAS_MTH07PCTPROF 7023 0.55 2 5 0 75 0
MAS_MTH08PCTPROF 7065 0.55 2 5 0 80 0
MAS_MTHHSPCTPROF 7983 0.49 2 5 0 81 0
MBL_MTH00PCTPROF 1951 0.88 1 5 0 126 0
MBL_MTH03PCTPROF 5389 0.66 2 5 0 109 0
MBL_MTH04PCTPROF 5307 0.66 2 5 0 108 0
MBL_MTH05PCTPROF 5249 0.67 2 5 0 118 0
MBL_MTH06PCTPROF 5239 0.67 2 5 0 114 0
MBL_MTH07PCTPROF 5579 0.65 1 5 0 115 0
MBL_MTH08PCTPROF 5618 0.64 1 5 0 122 0
MBL_MTHHSPCTPROF 6772 0.57 1 5 0 125 0
MHI_MTH00PCTPROF 1348 0.91 2 5 0 121 0
MHI_MTH03PCTPROF 4108 0.74 2 5 0 103 0
MHI_MTH04PCTPROF 4194 0.73 2 5 0 110 0
MHI_MTH05PCTPROF 4179 0.73 2 5 0 111 0
MHI_MTH06PCTPROF 4231 0.73 1 5 0 116 0
MHI_MTH07PCTPROF 4505 0.71 1 5 0 115 0
MHI_MTH08PCTPROF 4630 0.71 1 5 0 127 0
MHI_MTHHSPCTPROF 6008 0.62 1 5 0 118 0
MIG_MTH00PCTPROF 10326 0.34 2 5 0 85 0
MIG_MTH03PCTPROF 11597 0.26 2 5 0 35 0
MIG_MTH04PCTPROF 11613 0.26 2 5 0 36 0
MIG_MTH05PCTPROF 11628 0.26 2 5 0 36 0
MIG_MTH06PCTPROF 11676 0.26 2 5 0 36 0
MIG_MTH07PCTPROF 11814 0.25 2 5 0 30 0
MIG_MTH08PCTPROF 11817 0.25 2 5 0 37 0
MIG_MTHHSPCTPROF 12362 0.21 2 5 0 38 0
MTR_MTH00PCTPROF 9021 0.43 2 5 0 92 0
MTR_MTH03PCTPROF 10440 0.34 2 5 0 40 0
MTR_MTH04PCTPROF 10527 0.33 2 5 0 42 0
MTR_MTH05PCTPROF 10507 0.33 2 5 0 41 0
MTR_MTH06PCTPROF 10600 0.33 2 5 0 39 0
MTR_MTH07PCTPROF 10776 0.32 2 5 0 44 0
MTR_MTH08PCTPROF 10834 0.31 2 5 0 45 0
MTR_MTHHSPCTPROF 11647 0.26 2 5 0 47 0
MWH_MTH00PCTPROF 430 0.97 2 5 0 122 0
MWH_MTH03PCTPROF 1889 0.88 2 5 0 101 0
MWH_MTH04PCTPROF 1889 0.88 2 5 0 111 0
MWH_MTH05PCTPROF 1912 0.88 2 5 0 115 0
MWH_MTH06PCTPROF 1877 0.88 2 5 0 113 0
MWH_MTH07PCTPROF 2251 0.86 2 5 0 116 0
MWH_MTH08PCTPROF 2281 0.86 2 5 0 120 0
MWH_MTHHSPCTPROF 3955 0.75 2 5 0 119 0
M_MTH00PCTPROF 28 1.00 1 5 0 124 0
M_MTH03PCTPROF 1428 0.91 2 5 0 106 0
M_MTH04PCTPROF 1418 0.91 2 5 0 115 0
M_MTH05PCTPROF 1412 0.91 2 5 0 115 0
M_MTH06PCTPROF 1410 0.91 1 5 0 117 0
M_MTH07PCTPROF 1830 0.88 1 5 0 125 0
M_MTH08PCTPROF 1891 0.88 1 5 0 126 0
M_MTHHSPCTPROF 3712 0.76 1 5 0 122 0
STNAM 0 1.00 4 24 0 51 0
PIPELINE 0 1.00 38 38 0 1 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
ALL_MTH00NUMVALID 0 1.00 1627.91 6374.70 0 149 447.0 1284.00 355121 ▇▁▁▁▁
ALL_MTH03NUMVALID 1367 0.91 255.01 961.48 0 28 74.0 201.00 51775 ▇▁▁▁▁
ALL_MTH04NUMVALID 1368 0.91 254.95 951.13 0 28 75.0 202.00 51736 ▇▁▁▁▁
ALL_MTH05NUMVALID 1356 0.91 251.32 935.44 0 28 75.0 202.00 51537 ▇▁▁▁▁
ALL_MTH06NUMVALID 1354 0.91 249.75 918.15 0 28 76.0 204.00 49264 ▇▁▁▁▁
ALL_MTH07NUMVALID 1779 0.89 254.91 934.73 0 30 80.0 211.00 49880 ▇▁▁▁▁
ALL_MTH08NUMVALID 1836 0.88 258.21 929.41 0 30 81.0 214.00 50322 ▇▁▁▁▁
ALL_MTHHSNUMVALID 3666 0.77 325.95 1284.57 0 39 99.0 248.00 50607 ▇▁▁▁▁
CWD_MTH00NUMVALID 189 0.99 214.49 887.63 0 21 60.0 175.00 58764 ▇▁▁▁▁
CWD_MTH03NUMVALID 2014 0.87 35.38 138.93 0 4 11.0 29.00 9315 ▇▁▁▁▁
CWD_MTH04NUMVALID 2002 0.87 36.65 144.13 0 4 11.0 30.00 9704 ▇▁▁▁▁
CWD_MTH05NUMVALID 2006 0.87 36.25 143.00 0 4 11.0 30.00 9316 ▇▁▁▁▁
CWD_MTH06NUMVALID 2031 0.87 35.06 136.40 0 4 11.0 29.00 8970 ▇▁▁▁▁
CWD_MTH07NUMVALID 2384 0.85 34.82 139.01 0 4 11.0 29.00 9421 ▇▁▁▁▁
CWD_MTH08NUMVALID 2463 0.84 34.77 129.70 0 4 12.0 30.00 7754 ▇▁▁▁▁
CWD_MTHHSNUMVALID 4155 0.74 38.07 144.22 0 5 13.0 31.00 5312 ▇▁▁▁▁
ECD_MTH00NUMVALID 405 0.97 834.25 4424.40 0 64 182.0 517.00 293101 ▇▁▁▁▁
ECD_MTH03NUMVALID 1792 0.89 142.01 707.64 0 12 33.0 88.00 43322 ▇▁▁▁▁
ECD_MTH04NUMVALID 1776 0.89 139.39 691.80 0 12 33.0 87.50 43224 ▇▁▁▁▁
ECD_MTH05NUMVALID 1754 0.89 135.33 677.18 0 12 32.0 86.00 43027 ▇▁▁▁▁
ECD_MTH06NUMVALID 1772 0.89 131.76 657.63 0 12 32.0 86.00 41090 ▇▁▁▁▁
ECD_MTH07NUMVALID 2169 0.86 130.72 663.07 0 13 32.0 85.00 41130 ▇▁▁▁▁
ECD_MTH08NUMVALID 2215 0.86 128.30 645.08 0 12 31.0 83.00 41034 ▇▁▁▁▁
ECD_MTHHSNUMVALID 3952 0.75 137.66 717.20 0 14 33.0 85.50 40274 ▇▁▁▁▁
F_MTH00NUMVALID 47 1.00 797.18 3127.18 0 73 219.0 625.25 174078 ▇▁▁▁▁
F_MTH03NUMVALID 1459 0.91 124.90 469.39 0 14 36.0 98.00 25434 ▇▁▁▁▁
F_MTH04NUMVALID 1467 0.91 125.04 465.82 0 14 37.0 99.00 25386 ▇▁▁▁▁
F_MTH05NUMVALID 1463 0.91 123.30 458.45 0 14 37.0 100.00 25212 ▇▁▁▁▁
F_MTH06NUMVALID 1460 0.91 122.60 449.49 0 14 37.0 100.00 23946 ▇▁▁▁▁
F_MTH07NUMVALID 1877 0.88 125.05 457.22 0 15 39.0 103.00 24511 ▇▁▁▁▁
F_MTH08NUMVALID 1933 0.88 126.89 456.86 0 15 40.0 105.00 24535 ▇▁▁▁▁
F_MTHHSNUMVALID 3743 0.76 162.07 640.96 0 19 49.0 124.00 25054 ▇▁▁▁▁
HOM_MTH00NUMVALID 9382 0.40 36.95 173.69 0 0 3.0 15.00 5981 ▇▁▁▁▁
HOM_MTH03NUMVALID 10803 0.31 8.11 32.35 0 0 1.0 4.00 995 ▇▁▁▁▁
HOM_MTH04NUMVALID 10855 0.31 7.72 30.59 0 0 1.0 4.00 817 ▇▁▁▁▁
HOM_MTH05NUMVALID 10855 0.31 7.29 29.57 0 0 1.0 4.00 876 ▇▁▁▁▁
HOM_MTH06NUMVALID 10923 0.31 6.90 28.44 0 0 1.0 4.00 859 ▇▁▁▁▁
HOM_MTH07NUMVALID 11042 0.30 6.58 28.52 0 0 1.0 4.00 907 ▇▁▁▁▁
HOM_MTH08NUMVALID 11069 0.30 6.20 27.95 0 0 1.0 3.00 968 ▇▁▁▁▁
HOM_MTHHSNUMVALID 11704 0.26 7.04 32.67 0 0 1.0 4.00 1086 ▇▁▁▁▁
LEP_MTH00NUMVALID 4294 0.73 188.97 1270.02 0 2 10.0 56.00 95371 ▇▁▁▁▁
LEP_MTH03NUMVALID 6354 0.60 52.74 316.59 0 1 4.0 18.00 20260 ▇▁▁▁▁
LEP_MTH04NUMVALID 6505 0.59 44.48 268.76 0 1 3.0 15.00 17230 ▇▁▁▁▁
LEP_MTH05NUMVALID 6738 0.57 36.13 212.71 0 1 3.0 13.00 14017 ▇▁▁▁▁
LEP_MTH06NUMVALID 6940 0.56 30.29 169.92 0 1 3.0 12.00 10875 ▇▁▁▁▁
LEP_MTH07NUMVALID 7267 0.54 28.70 167.41 0 1 2.0 11.00 11046 ▇▁▁▁▁
LEP_MTH08NUMVALID 7406 0.53 26.58 159.77 0 1 2.0 10.00 10902 ▇▁▁▁▁
LEP_MTHHSNUMVALID 8633 0.45 28.18 189.85 0 0 2.0 10.00 11041 ▇▁▁▁▁
MAM_MTH00NUMVALID 4167 0.74 27.42 118.62 0 1 3.0 13.00 5454 ▇▁▁▁▁
MAM_MTH03NUMVALID 8056 0.49 5.96 21.55 0 0 1.0 4.00 846 ▇▁▁▁▁
MAM_MTH04NUMVALID 8050 0.49 5.96 22.13 0 0 1.0 4.00 930 ▇▁▁▁▁
MAM_MTH05NUMVALID 8049 0.49 5.94 21.47 0 0 1.0 4.00 842 ▇▁▁▁▁
MAM_MTH06NUMVALID 7977 0.49 5.83 20.34 0 0 1.0 4.00 796 ▇▁▁▁▁
MAM_MTH07NUMVALID 8141 0.48 5.85 20.12 0 0 1.0 4.00 749 ▇▁▁▁▁
MAM_MTH08NUMVALID 8129 0.48 5.74 19.84 0 0 1.0 4.00 738 ▇▁▁▁▁
MAM_MTHHSNUMVALID 8926 0.43 6.81 23.86 0 0 1.0 4.00 782 ▇▁▁▁▁
MAS_MTH00NUMVALID 3309 0.79 104.60 868.65 0 2 6.0 28.00 74447 ▇▁▁▁▁
MAS_MTH03NUMVALID 6732 0.57 20.82 144.82 0 1 2.0 8.00 10783 ▇▁▁▁▁
MAS_MTH04NUMVALID 6711 0.57 21.07 148.93 0 1 2.0 8.00 11147 ▇▁▁▁▁
MAS_MTH05NUMVALID 6747 0.57 19.93 142.78 0 1 2.0 7.00 10822 ▇▁▁▁▁
MAS_MTH06NUMVALID 6816 0.57 20.09 142.97 0 1 2.0 8.00 10584 ▇▁▁▁▁
MAS_MTH07NUMVALID 7023 0.55 20.70 143.74 0 1 2.0 8.00 10239 ▇▁▁▁▁
MAS_MTH08NUMVALID 7065 0.55 20.97 145.91 0 1 2.0 8.00 10299 ▇▁▁▁▁
MAS_MTHHSNUMVALID 7983 0.49 25.96 189.64 0 1 2.0 8.00 10573 ▇▁▁▁▁
MBL_MTH00NUMVALID 1951 0.88 296.34 1846.23 0 3 13.0 91.25 87843 ▇▁▁▁▁
MBL_MTH03NUMVALID 5389 0.66 56.56 310.58 0 1 4.0 24.00 13822 ▇▁▁▁▁
MBL_MTH04NUMVALID 5307 0.66 56.32 300.46 0 1 4.0 24.00 12539 ▇▁▁▁▁
MBL_MTH05NUMVALID 5249 0.67 54.86 295.57 0 1 4.0 23.00 12612 ▇▁▁▁▁
MBL_MTH06NUMVALID 5239 0.67 54.78 293.46 0 1 4.0 23.25 13169 ▇▁▁▁▁
MBL_MTH07NUMVALID 5579 0.65 55.27 294.07 0 1 4.0 25.00 12780 ▇▁▁▁▁
MBL_MTH08NUMVALID 5618 0.64 56.25 296.62 0 1 4.0 24.00 12982 ▇▁▁▁▁
MBL_MTHHSNUMVALID 6772 0.57 70.33 390.92 0 1 4.0 28.00 14271 ▇▁▁▁▁
MHI_MTH00NUMVALID 1348 0.91 407.63 3799.12 0 5 21.0 103.00 262592 ▇▁▁▁▁
MHI_MTH03NUMVALID 4108 0.74 76.74 618.54 0 1 5.0 24.00 38320 ▇▁▁▁▁
MHI_MTH04NUMVALID 4194 0.73 75.49 621.21 0 1 5.0 23.00 38689 ▇▁▁▁▁
MHI_MTH05NUMVALID 4179 0.73 73.55 611.80 0 1 5.0 23.00 38328 ▇▁▁▁▁
MHI_MTH06NUMVALID 4231 0.73 72.24 602.96 0 1 5.0 23.00 38729 ▇▁▁▁▁
MHI_MTH07NUMVALID 4505 0.71 72.75 620.83 0 1 5.0 22.00 40539 ▇▁▁▁▁
MHI_MTH08NUMVALID 4630 0.71 72.23 606.06 0 1 5.0 22.00 37538 ▇▁▁▁▁
MHI_MTHHSNUMVALID 6008 0.62 82.21 678.56 0 1 5.0 24.00 37326 ▇▁▁▁▁
MIG_MTH00NUMVALID 10326 0.34 24.42 105.06 0 0 1.0 8.00 2574 ▇▁▁▁▁
MIG_MTH03NUMVALID 11597 0.26 4.86 18.70 0 0 0.0 2.00 400 ▇▁▁▁▁
MIG_MTH04NUMVALID 11613 0.26 4.71 17.98 0 0 0.0 2.00 347 ▇▁▁▁▁
MIG_MTH05NUMVALID 11628 0.26 4.67 17.89 0 0 0.0 2.00 356 ▇▁▁▁▁
MIG_MTH06NUMVALID 11676 0.26 4.59 17.43 0 0 0.0 2.00 357 ▇▁▁▁▁
MIG_MTH07NUMVALID 11814 0.25 4.68 18.08 0 0 0.0 2.00 391 ▇▁▁▁▁
MIG_MTH08NUMVALID 11817 0.25 4.59 17.71 0 0 0.0 2.00 381 ▇▁▁▁▁
MIG_MTHHSNUMVALID 12362 0.21 5.43 22.77 0 0 0.0 2.00 379 ▇▁▁▁▁
MTR_MTH00NUMVALID 9021 0.43 45.37 173.09 0 2 8.0 28.00 6292 ▇▁▁▁▁
MTR_MTH03NUMVALID 10440 0.34 9.70 32.82 0 0 2.0 7.00 805 ▇▁▁▁▁
MTR_MTH04NUMVALID 10527 0.33 9.11 30.23 0 0 2.0 7.00 880 ▇▁▁▁▁
MTR_MTH05NUMVALID 10507 0.33 8.59 29.93 0 0 2.0 6.00 1044 ▇▁▁▁▁
MTR_MTH06NUMVALID 10600 0.33 8.31 28.75 0 0 2.0 6.00 1031 ▇▁▁▁▁
MTR_MTH07NUMVALID 10776 0.32 8.12 27.96 0 0 2.0 6.00 996 ▇▁▁▁▁
MTR_MTH08NUMVALID 10834 0.31 7.86 27.34 0 0 2.0 6.00 975 ▇▁▁▁▁
MTR_MTHHSNUMVALID 11647 0.26 9.60 47.00 0 0 1.5 6.00 1894 ▇▁▁▁▁
MWH_MTH00NUMVALID 430 0.97 890.47 2126.33 0 88 321.0 907.00 56161 ▇▁▁▁▁
MWH_MTH03NUMVALID 1889 0.88 136.04 303.38 0 17 54.0 140.00 8094 ▇▁▁▁▁
MWH_MTH04NUMVALID 1889 0.88 137.50 305.12 0 18 55.0 142.00 8120 ▇▁▁▁▁
MWH_MTH05NUMVALID 1912 0.88 137.61 301.39 0 18 55.0 143.00 8171 ▇▁▁▁▁
MWH_MTH06NUMVALID 1877 0.88 137.28 297.43 0 18 55.0 144.00 8009 ▇▁▁▁▁
MWH_MTH07NUMVALID 2251 0.86 140.82 298.64 0 19 58.0 149.00 8077 ▇▁▁▁▁
MWH_MTH08NUMVALID 2281 0.86 144.13 304.19 0 20 59.0 152.00 8123 ▇▁▁▁▁
MWH_MTHHSNUMVALID 3955 0.75 186.51 516.44 0 26 75.0 183.00 22170 ▇▁▁▁▁
M_MTH00NUMVALID 28 1.00 834.15 3254.34 0 77 229.0 661.00 181033 ▇▁▁▁▁
M_MTH03NUMVALID 1428 0.91 131.39 494.58 0 14 38.0 104.00 26338 ▇▁▁▁▁
M_MTH04NUMVALID 1418 0.91 131.16 487.66 0 14 38.0 104.00 26349 ▇▁▁▁▁
M_MTH05NUMVALID 1412 0.91 129.37 479.55 0 14 39.0 104.00 26324 ▇▁▁▁▁
M_MTH06NUMVALID 1410 0.91 128.48 471.16 0 14 39.0 105.00 25317 ▇▁▁▁▁
M_MTH07NUMVALID 1830 0.88 131.15 479.92 0 15 41.0 108.00 25369 ▇▁▁▁▁
M_MTH08NUMVALID 1891 0.88 132.66 475.00 0 16 42.0 111.00 25786 ▇▁▁▁▁
M_MTHHSNUMVALID 3712 0.76 165.46 647.22 0 20 51.0 127.00 25550 ▇▁▁▁▁
YEAR 0 1.00 2010.00 0.00 2010 2010 2010.0 2010.00 2010 ▁▁▇▁▁

Variable type: POSIXct

skim_variable n_missing complete_rate min max median n_unique
DL_INGESTION_DATETIME 0 1 2021-08-20 16:11:49 2021-08-20 16:11:49 2021-08-20 16:11:49 1
skim(rlalea_10)
Data summary
Name rlalea_10
Number of rows 15717
Number of columns 232
_______________________
Column type frequency:
character 118
numeric 113
POSIXct 1
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
ALL_RLA00PCTPROF 0 1.00 1 5 0 132 0
ALL_RLA03PCTPROF 1339 0.91 2 5 0 123 0
ALL_RLA04PCTPROF 1342 0.91 2 5 0 117 0
ALL_RLA05PCTPROF 1335 0.92 2 5 0 118 0
ALL_RLA06PCTPROF 1333 0.92 2 5 0 116 0
ALL_RLA07PCTPROF 1770 0.89 2 5 0 117 0
ALL_RLA08PCTPROF 1818 0.88 2 5 0 117 0
ALL_RLAHSPCTPROF 3683 0.77 2 5 0 118 0
CWD_RLA00PCTPROF 187 0.99 1 5 0 127 0
CWD_RLA03PCTPROF 1996 0.87 2 5 0 118 0
CWD_RLA04PCTPROF 1986 0.87 1 5 0 109 0
CWD_RLA05PCTPROF 1994 0.87 1 5 0 117 0
CWD_RLA06PCTPROF 2018 0.87 1 5 0 109 0
CWD_RLA07PCTPROF 2374 0.85 1 5 0 102 0
CWD_RLA08PCTPROF 2448 0.84 2 5 0 101 0
CWD_RLAHSPCTPROF 4133 0.74 1 5 0 99 0
ECD_RLA00PCTPROF 401 0.97 1 5 0 123 0
ECD_RLA03PCTPROF 1766 0.89 2 5 0 121 0
ECD_RLA04PCTPROF 1749 0.89 2 5 0 115 0
ECD_RLA05PCTPROF 1731 0.89 2 5 0 114 0
ECD_RLA06PCTPROF 1749 0.89 2 5 0 114 0
ECD_RLA07PCTPROF 2155 0.86 2 5 0 113 0
ECD_RLA08PCTPROF 2198 0.86 2 5 0 120 0
ECD_RLAHSPCTPROF 3966 0.75 2 5 0 119 0
FIPST 0 1.00 2 2 0 51 0
F_RLA00PCTPROF 40 1.00 2 5 0 114 0
F_RLA03PCTPROF 1433 0.91 2 5 0 118 0
F_RLA04PCTPROF 1441 0.91 2 5 0 108 0
F_RLA05PCTPROF 1442 0.91 2 5 0 104 0
F_RLA06PCTPROF 1440 0.91 2 5 0 105 0
F_RLA07PCTPROF 1866 0.88 2 5 0 105 0
F_RLA08PCTPROF 1916 0.88 2 5 0 106 0
F_RLAHSPCTPROF 3752 0.76 2 5 0 108 0
FILEURL 0 1.00 87 87 0 1 0
HOM_RLA00PCTPROF 9576 0.39 2 5 0 94 0
HOM_RLA03PCTPROF 11027 0.30 2 5 0 42 0
HOM_RLA04PCTPROF 11079 0.30 2 5 0 41 0
HOM_RLA05PCTPROF 11078 0.30 2 5 0 40 0
HOM_RLA06PCTPROF 11164 0.29 2 5 0 39 0
HOM_RLA07PCTPROF 11285 0.28 2 5 0 38 0
HOM_RLA08PCTPROF 11324 0.28 2 5 0 40 0
HOM_RLAHSPCTPROF 11942 0.24 2 5 0 44 0
LEAID 0 1.00 7 7 0 15717 0
LEANM09 0 1.00 3 60 0 15436 0
LEP_RLA00PCTPROF 4442 0.72 1 5 0 125 0
LEP_RLA03PCTPROF 6479 0.59 1 5 0 119 0
LEP_RLA04PCTPROF 6656 0.58 1 5 0 109 0
LEP_RLA05PCTPROF 6902 0.56 1 5 0 110 0
LEP_RLA06PCTPROF 7103 0.55 1 5 0 107 0
LEP_RLA07PCTPROF 7462 0.53 1 5 0 99 0
LEP_RLA08PCTPROF 7576 0.52 1 5 0 94 0
LEP_RLAHSPCTPROF 8855 0.44 1 5 0 84 0
MAM_RLA00PCTPROF 4231 0.73 2 5 0 98 0
MAM_RLA03PCTPROF 8186 0.48 2 5 0 43 0
MAM_RLA04PCTPROF 8196 0.48 2 5 0 41 0
MAM_RLA05PCTPROF 8182 0.48 2 5 0 38 0
MAM_RLA06PCTPROF 8120 0.48 2 5 0 41 0
MAM_RLA07PCTPROF 8294 0.47 2 5 0 40 0
MAM_RLA08PCTPROF 8276 0.47 2 5 0 38 0
MAM_RLAHSPCTPROF 9114 0.42 2 5 0 40 0
MAS_RLA00PCTPROF 3360 0.79 2 5 0 96 0
MAS_RLA03PCTPROF 6857 0.56 2 5 0 81 0
MAS_RLA04PCTPROF 6853 0.56 2 5 0 71 0
MAS_RLA05PCTPROF 6889 0.56 2 5 0 74 0
MAS_RLA06PCTPROF 6939 0.56 2 5 0 76 0
MAS_RLA07PCTPROF 7174 0.54 2 5 0 76 0
MAS_RLA08PCTPROF 7188 0.54 2 5 0 74 0
MAS_RLAHSPCTPROF 8130 0.48 2 5 0 80 0
MBL_RLA00PCTPROF 1962 0.88 2 5 0 122 0
MBL_RLA03PCTPROF 5449 0.65 2 5 0 116 0
MBL_RLA04PCTPROF 5362 0.66 2 5 0 112 0
MBL_RLA05PCTPROF 5311 0.66 2 5 0 115 0
MBL_RLA06PCTPROF 5307 0.66 2 5 0 113 0
MBL_RLA07PCTPROF 5649 0.64 2 5 0 113 0
MBL_RLA08PCTPROF 5681 0.64 2 5 0 117 0
MBL_RLAHSPCTPROF 6844 0.56 2 5 0 118 0
MHI_RLA00PCTPROF 1357 0.91 2 5 0 117 0
MHI_RLA03PCTPROF 4127 0.74 2 5 0 120 0
MHI_RLA04PCTPROF 4222 0.73 2 5 0 109 0
MHI_RLA05PCTPROF 4208 0.73 2 5 0 106 0
MHI_RLA06PCTPROF 4278 0.73 2 5 0 107 0
MHI_RLA07PCTPROF 4551 0.71 2 5 0 106 0
MHI_RLA08PCTPROF 4683 0.70 2 5 0 115 0
MHI_RLAHSPCTPROF 6085 0.61 2 5 0 110 0
MIG_RLA00PCTPROF 10414 0.34 2 5 0 76 0
MIG_RLA03PCTPROF 11733 0.25 2 5 0 39 0
MIG_RLA04PCTPROF 11748 0.25 2 5 0 32 0
MIG_RLA05PCTPROF 11765 0.25 2 5 0 33 0
MIG_RLA06PCTPROF 11817 0.25 2 5 0 30 0
MIG_RLA07PCTPROF 11972 0.24 2 5 0 33 0
MIG_RLA08PCTPROF 11969 0.24 2 5 0 35 0
MIG_RLAHSPCTPROF 12556 0.20 2 5 0 38 0
MTR_RLA00PCTPROF 8836 0.44 2 5 0 91 0
MTR_RLA03PCTPROF 10319 0.34 2 5 0 45 0
MTR_RLA04PCTPROF 10417 0.34 2 5 0 39 0
MTR_RLA05PCTPROF 10409 0.34 2 5 0 35 0
MTR_RLA06PCTPROF 10510 0.33 2 5 0 36 0
MTR_RLA07PCTPROF 10696 0.32 2 5 0 40 0
MTR_RLA08PCTPROF 10732 0.32 2 5 0 39 0
MTR_RLAHSPCTPROF 11549 0.27 2 5 0 48 0
MWH_RLA00PCTPROF 421 0.97 2 5 0 109 0
MWH_RLA03PCTPROF 1861 0.88 2 5 0 100 0
MWH_RLA04PCTPROF 1865 0.88 2 5 0 103 0
MWH_RLA05PCTPROF 1895 0.88 2 5 0 98 0
MWH_RLA06PCTPROF 1853 0.88 2 5 0 96 0
MWH_RLA07PCTPROF 2243 0.86 2 5 0 99 0
MWH_RLA08PCTPROF 2264 0.86 2 5 0 103 0
MWH_RLAHSPCTPROF 3967 0.75 2 5 0 101 0
M_RLA00PCTPROF 24 1.00 1 5 0 122 0
M_RLA03PCTPROF 1399 0.91 2 5 0 120 0
M_RLA04PCTPROF 1392 0.91 2 5 0 116 0
M_RLA05PCTPROF 1390 0.91 2 5 0 114 0
M_RLA06PCTPROF 1390 0.91 2 5 0 116 0
M_RLA07PCTPROF 1819 0.88 2 5 0 117 0
M_RLA08PCTPROF 1876 0.88 2 5 0 117 0
M_RLAHSPCTPROF 3725 0.76 2 5 0 116 0
STNAM 0 1.00 4 24 0 51 0
PIPELINE 0 1.00 37 37 0 1 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
ALL_RLA00NUMVALID 0 1.00 1619.22 6341.20 0 150 445.0 1280.00 354300 ▇▁▁▁▁
ALL_RLA03NUMVALID 1339 0.91 254.95 959.86 0 28 74.0 200.00 51708 ▇▁▁▁▁
ALL_RLA04NUMVALID 1342 0.91 254.90 950.06 0 28 74.0 202.00 51714 ▇▁▁▁▁
ALL_RLA05NUMVALID 1335 0.92 251.72 937.02 0 28 75.0 203.00 51447 ▇▁▁▁▁
ALL_RLA06NUMVALID 1333 0.92 250.40 919.39 0 28 76.0 205.00 49186 ▇▁▁▁▁
ALL_RLA07NUMVALID 1770 0.89 257.32 940.54 0 30 80.0 212.00 49820 ▇▁▁▁▁
ALL_RLA08NUMVALID 1818 0.88 258.98 933.11 0 30 81.0 214.00 50401 ▇▁▁▁▁
ALL_RLAHSNUMVALID 3683 0.77 308.21 1190.43 0 38 97.0 242.00 50027 ▇▁▁▁▁
CWD_RLA00NUMVALID 187 0.99 213.74 882.36 0 21 60.0 174.00 58700 ▇▁▁▁▁
CWD_RLA03NUMVALID 1996 0.87 35.38 138.53 0 4 11.0 29.00 9298 ▇▁▁▁▁
CWD_RLA04NUMVALID 1986 0.87 36.67 143.98 0 4 11.0 30.00 9681 ▇▁▁▁▁
CWD_RLA05NUMVALID 1994 0.87 36.30 142.77 0 4 11.0 30.00 9305 ▇▁▁▁▁
CWD_RLA06NUMVALID 2018 0.87 35.11 136.29 0 4 11.0 29.00 8980 ▇▁▁▁▁
CWD_RLA07NUMVALID 2374 0.85 34.99 139.13 0 4 11.0 29.00 9404 ▇▁▁▁▁
CWD_RLA08NUMVALID 2448 0.84 35.00 130.36 0 4 12.0 30.00 7758 ▇▁▁▁▁
CWD_RLAHSNUMVALID 4133 0.74 36.27 134.64 0 5 12.0 30.00 5096 ▇▁▁▁▁
ECD_RLA00NUMVALID 401 0.97 829.43 4415.33 0 64 182.0 511.00 292359 ▇▁▁▁▁
ECD_RLA03NUMVALID 1766 0.89 141.80 705.93 0 12 33.0 88.00 43268 ▇▁▁▁▁
ECD_RLA04NUMVALID 1749 0.89 139.17 690.60 0 12 33.0 87.00 43213 ▇▁▁▁▁
ECD_RLA05NUMVALID 1731 0.89 135.21 675.88 0 12 32.0 86.00 42939 ▇▁▁▁▁
ECD_RLA06NUMVALID 1749 0.89 131.70 656.48 0 12 32.0 86.00 41010 ▇▁▁▁▁
ECD_RLA07NUMVALID 2155 0.86 131.24 662.80 0 13 33.0 86.00 41048 ▇▁▁▁▁
ECD_RLA08NUMVALID 2198 0.86 128.42 645.67 0 12 31.0 83.00 41069 ▇▁▁▁▁
ECD_RLAHSNUMVALID 3966 0.75 130.61 707.05 0 14 32.0 81.00 39812 ▇▁▁▁▁
F_RLA00NUMVALID 40 1.00 791.94 3108.78 0 73 219.0 622.00 173641 ▇▁▁▁▁
F_RLA03NUMVALID 1433 0.91 124.80 468.57 0 14 37.0 98.00 25419 ▇▁▁▁▁
F_RLA04NUMVALID 1441 0.91 124.92 465.21 0 14 37.0 99.00 25380 ▇▁▁▁▁
F_RLA05NUMVALID 1442 0.91 123.40 459.09 0 14 37.0 100.00 25179 ▇▁▁▁▁
F_RLA06NUMVALID 1440 0.91 122.83 449.91 0 14 38.0 100.00 23918 ▇▁▁▁▁
F_RLA07NUMVALID 1866 0.88 126.15 459.87 0 15 40.0 104.00 24487 ▇▁▁▁▁
F_RLA08NUMVALID 1916 0.88 127.14 458.45 0 15 40.0 105.00 24581 ▇▁▁▁▁
F_RLAHSNUMVALID 3752 0.76 153.11 594.10 0 19 48.0 120.00 24819 ▇▁▁▁▁
HOM_RLA00NUMVALID 9576 0.39 36.96 176.55 0 0 3.0 15.00 5967 ▇▁▁▁▁
HOM_RLA03NUMVALID 11027 0.30 8.34 33.04 0 0 1.0 4.00 990 ▇▁▁▁▁
HOM_RLA04NUMVALID 11079 0.30 7.92 31.23 0 0 1.0 4.00 811 ▇▁▁▁▁
HOM_RLA05NUMVALID 11078 0.30 7.47 30.26 0 0 1.0 4.00 878 ▇▁▁▁▁
HOM_RLA06NUMVALID 11164 0.29 7.09 29.13 0 0 1.0 4.00 853 ▇▁▁▁▁
HOM_RLA07NUMVALID 11285 0.28 6.80 29.23 0 0 1.0 4.00 906 ▇▁▁▁▁
HOM_RLA08NUMVALID 11324 0.28 6.44 28.86 0 0 1.0 3.00 965 ▇▁▁▁▁
HOM_RLAHSNUMVALID 11942 0.24 6.82 33.73 0 0 1.0 4.00 1091 ▇▁▁▁▁
LEP_RLA00NUMVALID 4442 0.72 188.13 1262.64 0 2 10.0 56.00 94962 ▇▁▁▁▁
LEP_RLA03NUMVALID 6479 0.59 53.05 316.84 0 1 4.0 18.00 20195 ▇▁▁▁▁
LEP_RLA04NUMVALID 6656 0.58 44.83 269.53 0 1 3.0 16.00 17209 ▇▁▁▁▁
LEP_RLA05NUMVALID 6902 0.56 36.41 213.16 0 1 3.0 13.00 13948 ▇▁▁▁▁
LEP_RLA06NUMVALID 7103 0.55 30.46 170.11 0 1 3.0 12.00 10814 ▇▁▁▁▁
LEP_RLA07NUMVALID 7462 0.53 28.96 167.96 0 1 3.0 11.00 10996 ▇▁▁▁▁
LEP_RLA08NUMVALID 7576 0.52 26.81 161.03 0 1 2.0 10.00 10893 ▇▁▁▁▁
LEP_RLAHSNUMVALID 8855 0.44 26.85 179.57 0 0 2.0 9.00 10907 ▇▁▁▁▁
MAM_RLA00NUMVALID 4231 0.73 27.49 118.97 0 1 4.0 13.00 5481 ▇▁▁▁▁
MAM_RLA03NUMVALID 8186 0.48 6.08 21.75 0 0 1.0 4.00 846 ▇▁▁▁▁
MAM_RLA04NUMVALID 8196 0.48 6.09 22.35 0 0 1.0 4.00 930 ▇▁▁▁▁
MAM_RLA05NUMVALID 8182 0.48 6.07 21.69 0 0 1.0 4.00 843 ▇▁▁▁▁
MAM_RLA06NUMVALID 8120 0.48 5.96 20.52 0 0 1.0 4.00 796 ▇▁▁▁▁
MAM_RLA07NUMVALID 8294 0.47 6.01 20.35 0 0 1.0 4.00 750 ▇▁▁▁▁
MAM_RLA08NUMVALID 8276 0.47 5.89 20.09 0 0 1.0 4.00 738 ▇▁▁▁▁
MAM_RLAHSNUMVALID 9114 0.42 6.76 23.98 0 0 1.0 4.00 782 ▇▁▁▁▁
MAS_RLA00NUMVALID 3360 0.79 103.96 863.16 0 2 6.0 28.00 74473 ▇▁▁▁▁
MAS_RLA03NUMVALID 6857 0.56 21.02 145.69 0 1 2.0 8.00 10786 ▇▁▁▁▁
MAS_RLA04NUMVALID 6853 0.56 21.32 150.02 0 1 2.0 8.00 11155 ▇▁▁▁▁
MAS_RLA05NUMVALID 6889 0.56 20.27 145.00 0 1 2.0 8.00 10818 ▇▁▁▁▁
MAS_RLA06NUMVALID 6939 0.56 20.43 144.87 0 1 2.0 8.00 10588 ▇▁▁▁▁
MAS_RLA07NUMVALID 7174 0.54 21.26 146.45 0 1 2.0 8.00 10240 ▇▁▁▁▁
MAS_RLA08NUMVALID 7188 0.54 21.32 147.73 0 1 2.0 8.00 10306 ▇▁▁▁▁
MAS_RLAHSNUMVALID 8130 0.48 24.73 165.73 0 1 2.0 8.00 10580 ▇▁▁▁▁
MBL_RLA00NUMVALID 1962 0.88 293.97 1840.98 0 3 13.0 91.00 87980 ▇▁▁▁▁
MBL_RLA03NUMVALID 5449 0.65 57.02 311.92 0 1 4.0 24.00 13865 ▇▁▁▁▁
MBL_RLA04NUMVALID 5362 0.66 56.73 301.63 0 1 4.0 24.00 12579 ▇▁▁▁▁
MBL_RLA05NUMVALID 5311 0.66 55.35 296.99 0 1 4.0 24.00 12641 ▇▁▁▁▁
MBL_RLA06NUMVALID 5307 0.66 55.42 295.19 0 1 4.0 24.00 13179 ▇▁▁▁▁
MBL_RLA07NUMVALID 5649 0.64 56.54 297.15 0 1 4.0 25.00 12789 ▇▁▁▁▁
MBL_RLA08NUMVALID 5681 0.64 56.73 297.96 0 1 4.0 25.00 13004 ▇▁▁▁▁
MBL_RLAHSNUMVALID 6844 0.56 65.27 371.34 0 1 4.0 27.00 14365 ▇▁▁▁▁
MHI_RLA00NUMVALID 1357 0.91 406.41 3795.81 0 5 21.0 104.00 261952 ▇▁▁▁▁
MHI_RLA03NUMVALID 4127 0.74 76.85 618.33 0 1 5.0 24.00 38267 ▇▁▁▁▁
MHI_RLA04NUMVALID 4222 0.73 75.69 621.80 0 1 5.0 23.00 38633 ▇▁▁▁▁
MHI_RLA05NUMVALID 4208 0.73 73.77 612.11 0 1 5.0 23.00 38276 ▇▁▁▁▁
MHI_RLA06NUMVALID 4278 0.73 72.55 603.84 0 1 5.0 23.00 38702 ▇▁▁▁▁
MHI_RLA07NUMVALID 4551 0.71 73.20 621.94 0 1 5.0 23.00 40489 ▇▁▁▁▁
MHI_RLA08NUMVALID 4683 0.70 72.85 608.43 0 1 5.0 23.00 37511 ▇▁▁▁▁
MHI_RLAHSNUMVALID 6085 0.61 80.49 677.09 0 1 5.0 24.00 36925 ▇▁▁▁▁
MIG_RLA00NUMVALID 10414 0.34 24.68 105.48 0 0 1.0 8.00 2570 ▇▁▁▁▁
MIG_RLA03NUMVALID 11733 0.25 5.03 18.98 0 0 0.0 2.00 400 ▇▁▁▁▁
MIG_RLA04NUMVALID 11748 0.25 4.89 18.28 0 0 0.0 2.00 348 ▇▁▁▁▁
MIG_RLA05NUMVALID 11765 0.25 4.85 18.19 0 0 0.0 2.00 356 ▇▁▁▁▁
MIG_RLA06NUMVALID 11817 0.25 4.78 17.74 0 0 0.0 2.00 357 ▇▁▁▁▁
MIG_RLA07NUMVALID 11972 0.24 4.90 18.45 0 0 0.0 2.00 392 ▇▁▁▁▁
MIG_RLA08NUMVALID 11969 0.24 4.80 18.13 0 0 0.0 2.00 379 ▇▁▁▁▁
MIG_RLAHSNUMVALID 12556 0.20 5.46 22.90 0 0 0.0 2.00 375 ▇▁▁▁▁
MTR_RLA00NUMVALID 8836 0.44 44.81 171.75 0 2 7.0 28.00 6302 ▇▁▁▁▁
MTR_RLA03NUMVALID 10319 0.34 9.63 32.58 0 0 2.0 7.00 806 ▇▁▁▁▁
MTR_RLA04NUMVALID 10417 0.34 9.07 30.05 0 0 2.0 7.00 880 ▇▁▁▁▁
MTR_RLA05NUMVALID 10409 0.34 8.56 29.77 0 0 2.0 6.00 1045 ▇▁▁▁▁
MTR_RLA06NUMVALID 10510 0.33 8.29 28.61 0 0 2.0 6.00 1028 ▇▁▁▁▁
MTR_RLA07NUMVALID 10696 0.32 8.11 27.93 0 0 2.0 6.00 1005 ▇▁▁▁▁
MTR_RLA08NUMVALID 10732 0.32 7.85 27.28 0 0 2.0 6.00 977 ▇▁▁▁▁
MTR_RLAHSNUMVALID 11549 0.27 9.55 46.52 0 0 2.0 6.00 1880 ▇▁▁▁▁
MWH_RLA00NUMVALID 421 0.97 884.81 2103.53 0 88 319.0 901.25 56235 ▇▁▁▁▁
MWH_RLA03NUMVALID 1861 0.88 136.04 303.28 0 17 54.0 140.00 8099 ▇▁▁▁▁
MWH_RLA04NUMVALID 1865 0.88 137.51 305.06 0 18 55.0 143.00 8122 ▇▁▁▁▁
MWH_RLA05NUMVALID 1895 0.88 137.95 304.15 0 18 55.0 143.00 8173 ▇▁▁▁▁
MWH_RLA06NUMVALID 1853 0.88 137.73 299.82 0 18 55.5 145.00 8013 ▇▁▁▁▁
MWH_RLA07NUMVALID 2243 0.86 142.37 305.10 0 19 59.0 150.00 8081 ▇▁▁▁▁
MWH_RLA08NUMVALID 2264 0.86 144.59 308.40 0 20 59.0 152.00 8171 ▇▁▁▁▁
MWH_RLAHSNUMVALID 3967 0.75 175.70 441.50 0 26 73.0 178.00 12213 ▇▁▁▁▁
M_RLA00NUMVALID 24 1.00 829.02 3236.33 0 77 229.0 657.00 180650 ▇▁▁▁▁
M_RLA03NUMVALID 1399 0.91 131.21 493.41 0 14 38.0 104.00 26286 ▇▁▁▁▁
M_RLA04NUMVALID 1392 0.91 131.01 486.85 0 14 38.0 104.00 26333 ▇▁▁▁▁
M_RLA05NUMVALID 1390 0.91 129.47 480.15 0 14 39.0 104.00 26267 ▇▁▁▁▁
M_RLA06NUMVALID 1390 0.91 128.73 471.69 0 14 39.0 105.00 25267 ▇▁▁▁▁
M_RLA07NUMVALID 1819 0.88 132.24 482.73 0 16 41.0 108.00 25333 ▇▁▁▁▁
M_RLA08NUMVALID 1876 0.88 133.03 476.86 0 16 42.0 111.00 25819 ▇▁▁▁▁
M_RLAHSNUMVALID 3725 0.76 156.44 599.42 0 20 50.0 123.00 25345 ▇▁▁▁▁
YEAR 0 1.00 2010.00 0.00 2010 2010 2010.0 2010.00 2010 ▁▁▇▁▁

Variable type: POSIXct

skim_variable n_missing complete_rate min max median n_unique
DL_INGESTION_DATETIME 0 1 2021-08-20 16:11:49 2021-08-20 16:11:49 2021-08-20 16:11:49 1
mathlea_10 %>% tabyl(ECD_MTHHSPCTPROF)
##  ECD_MTHHSPCTPROF    n      percent valid_percent
##                10    1 6.350416e-05  8.478169e-05
##             10-14   53 3.365720e-03  4.493429e-03
##             11-19  111 7.048962e-03  9.410767e-03
##                12    1 6.350416e-05  8.478169e-05
##                14    4 2.540166e-04  3.391267e-04
##                15    2 1.270083e-04  1.695634e-04
##             15-19   89 5.651870e-03  7.545570e-03
##                16    1 6.350416e-05  8.478169e-05
##                17    2 1.270083e-04  1.695634e-04
##                18    1 6.350416e-05  8.478169e-05
##                19    8 5.080333e-04  6.782535e-04
##                20    2 1.270083e-04  1.695634e-04
##             20-24  133 8.446053e-03  1.127596e-02
##             20-29  176 1.117673e-02  1.492158e-02
##                21    6 3.810250e-04  5.086901e-04
##             21-39  392 2.489363e-02  3.323442e-02
##                22    6 3.810250e-04  5.086901e-04
##                23    7 4.445291e-04  5.934718e-04
##                24    7 4.445291e-04  5.934718e-04
##                25    6 3.810250e-04  5.086901e-04
##             25-29  175 1.111323e-02  1.483680e-02
##                26   12 7.620499e-04  1.017380e-03
##                27    8 5.080333e-04  6.782535e-04
##                28    8 5.080333e-04  6.782535e-04
##                29    4 2.540166e-04  3.391267e-04
##                 3    1 6.350416e-05  8.478169e-05
##                30   21 1.333587e-03  1.780415e-03
##             30-34  215 1.365339e-02  1.822806e-02
##             30-39  295 1.873373e-02  2.501060e-02
##                31   12 7.620499e-04  1.017380e-03
##                32   12 7.620499e-04  1.017380e-03
##                33   14 8.890582e-04  1.186944e-03
##                34   18 1.143075e-03  1.526070e-03
##                35   15 9.525624e-04  1.271725e-03
##             35-39  202 1.282784e-02  1.712590e-02
##                36   14 8.890582e-04  1.186944e-03
##                37   19 1.206579e-03  1.610852e-03
##                38   20 1.270083e-03  1.695634e-03
##                39   15 9.525624e-04  1.271725e-03
##                40   18 1.143075e-03  1.526070e-03
##             40-44  226 1.435194e-02  1.916066e-02
##             40-49  280 1.778116e-02  2.373887e-02
##             40-59  582 3.695942e-02  4.934294e-02
##                41   24 1.524100e-03  2.034760e-03
##                42   19 1.206579e-03  1.610852e-03
##                43   15 9.525624e-04  1.271725e-03
##                44   12 7.620499e-04  1.017380e-03
##                45   14 8.890582e-04  1.186944e-03
##             45-49  245 1.555852e-02  2.077151e-02
##                46   14 8.890582e-04  1.186944e-03
##                47   10 6.350416e-04  8.478169e-04
##                48   19 1.206579e-03  1.610852e-03
##                49   19 1.206579e-03  1.610852e-03
##                50   14 8.890582e-04  1.186944e-03
##             50-54  215 1.365339e-02  1.822806e-02
##             50-59  317 2.013082e-02  2.687579e-02
##                51   16 1.016067e-03  1.356507e-03
##                52   11 6.985458e-04  9.325986e-04
##                53   17 1.079571e-03  1.441289e-03
##                54   15 9.525624e-04  1.271725e-03
##                55   15 9.525624e-04  1.271725e-03
##             55-59  211 1.339938e-02  1.788894e-02
##                56   19 1.206579e-03  1.610852e-03
##                57   13 8.255541e-04  1.102162e-03
##                58   18 1.143075e-03  1.526070e-03
##                59   19 1.206579e-03  1.610852e-03
##                 6    1 6.350416e-05  8.478169e-05
##               6-9   15 9.525624e-04  1.271725e-03
##                60   18 1.143075e-03  1.526070e-03
##             60-64  250 1.587604e-02  2.119542e-02
##             60-69  358 2.273449e-02  3.035184e-02
##             60-79  658 4.178574e-02  5.578635e-02
##                61    9 5.715374e-04  7.630352e-04
##                62   19 1.206579e-03  1.610852e-03
##                63   18 1.143075e-03  1.526070e-03
##                64   12 7.620499e-04  1.017380e-03
##                65   19 1.206579e-03  1.610852e-03
##             65-69  248 1.574903e-02  2.102586e-02
##                66   12 7.620499e-04  1.017380e-03
##                67   15 9.525624e-04  1.271725e-03
##                68   15 9.525624e-04  1.271725e-03
##                69   12 7.620499e-04  1.017380e-03
##                70   16 1.016067e-03  1.356507e-03
##             70-74  225 1.428844e-02  1.907588e-02
##             70-79  298 1.892424e-02  2.526494e-02
##                71   11 6.985458e-04  9.325986e-04
##                72   14 8.890582e-04  1.186944e-03
##                73    8 5.080333e-04  6.782535e-04
##                74    9 5.715374e-04  7.630352e-04
##                75    9 5.715374e-04  7.630352e-04
##             75-79  168 1.066870e-02  1.424332e-02
##                76    9 5.715374e-04  7.630352e-04
##                77    7 4.445291e-04  5.934718e-04
##                78   11 6.985458e-04  9.325986e-04
##                79    7 4.445291e-04  5.934718e-04
##                 8    1 6.350416e-05  8.478169e-05
##                80    3 1.905125e-04  2.543451e-04
##             80-84  109 6.921953e-03  9.241204e-03
##             80-89  244 1.549501e-02  2.068673e-02
##                81    8 5.080333e-04  6.782535e-04
##                82    5 3.175208e-04  4.239084e-04
##                83    5 3.175208e-04  4.239084e-04
##                84    7 4.445291e-04  5.934718e-04
##                85    3 1.905125e-04  2.543451e-04
##             85-89   93 5.905887e-03  7.884697e-03
##                86    5 3.175208e-04  4.239084e-04
##                87   10 6.350416e-04  8.478169e-04
##                88    8 5.080333e-04  6.782535e-04
##                89   11 6.985458e-04  9.325986e-04
##                 9    1 6.350416e-05  8.478169e-05
##                90    3 1.905125e-04  2.543451e-04
##             90-94   54 3.429225e-03  4.578211e-03
##                91    4 2.540166e-04  3.391267e-04
##                92    4 2.540166e-04  3.391267e-04
##                93    6 3.810250e-04  5.086901e-04
##                94    3 1.905125e-04  2.543451e-04
##                95    2 1.270083e-04  1.695634e-04
##                96    2 1.270083e-04  1.695634e-04
##                97    1 6.350416e-05  8.478169e-05
##                98    1 6.350416e-05  8.478169e-05
##              GE50 1521 9.658983e-02  1.289529e-01
##              GE80  456 2.895790e-02  3.866045e-02
##              GE90  113 7.175970e-03  9.580331e-03
##              GE95   23 1.460596e-03  1.949979e-03
##              LE10   58 3.683241e-03  4.917338e-03
##              LE20  204 1.295485e-02  1.729546e-02
##               LE5   13 8.255541e-04  1.102162e-03
##              LT50  817 5.188290e-02  6.926664e-02
##               n/a   66 4.191275e-03  5.595591e-03
##                PS  975 6.191656e-02  8.266214e-02
##              <NA> 3952 2.509684e-01            NA
mathlea_10 %>% tabyl(LEP_MTHHSPCTPROF)
##  LEP_MTHHSPCTPROF    n      percent valid_percent
##                10    1 6.350416e-05  0.0001405679
##             10-14   59 3.746745e-03  0.0082935058
##                11    3 1.905125e-04  0.0004217037
##             11-19   70 4.445291e-03  0.0098397526
##                12    4 2.540166e-04  0.0005622716
##                13    3 1.905125e-04  0.0004217037
##                14    8 5.080333e-04  0.0011245432
##                15    4 2.540166e-04  0.0005622716
##             15-19   61 3.873754e-03  0.0085746416
##                16    5 3.175208e-04  0.0007028395
##                17    4 2.540166e-04  0.0005622716
##                18    2 1.270083e-04  0.0002811358
##                19    7 4.445291e-04  0.0009839753
##                20    5 3.175208e-04  0.0007028395
##             20-24   49 3.111704e-03  0.0068878268
##             20-29   62 3.937258e-03  0.0087152094
##                21    5 3.175208e-04  0.0007028395
##             21-39  122 7.747507e-03  0.0171492831
##                22    6 3.810250e-04  0.0008434074
##                23    1 6.350416e-05  0.0001405679
##                24    2 1.270083e-04  0.0002811358
##                25    5 3.175208e-04  0.0007028395
##             25-29   40 2.540166e-03  0.0056227158
##                26    6 3.810250e-04  0.0008434074
##                28    6 3.810250e-04  0.0008434074
##                29    2 1.270083e-04  0.0002811358
##             30-34   19 1.206579e-03  0.0026707900
##             30-39   48 3.048200e-03  0.0067472589
##                31    3 1.905125e-04  0.0004217037
##                34    2 1.270083e-04  0.0002811358
##             35-39   26 1.651108e-03  0.0036547653
##                36    4 2.540166e-04  0.0005622716
##                37    1 6.350416e-05  0.0001405679
##                39    2 1.270083e-04  0.0002811358
##                40    1 6.350416e-05  0.0001405679
##             40-44   22 1.397092e-03  0.0030924937
##             40-49   33 2.095637e-03  0.0046387405
##             40-59   90 5.715374e-03  0.0126511105
##                42    1 6.350416e-05  0.0001405679
##                44    1 6.350416e-05  0.0001405679
##                45    3 1.905125e-04  0.0004217037
##             45-49   18 1.143075e-03  0.0025302221
##                46    2 1.270083e-04  0.0002811358
##                48    1 6.350416e-05  0.0001405679
##                49    1 6.350416e-05  0.0001405679
##             50-54   21 1.333587e-03  0.0029519258
##             50-59   31 1.968629e-03  0.0043576047
##                53    1 6.350416e-05  0.0001405679
##             55-59   14 8.890582e-04  0.0019679505
##                 6    1 6.350416e-05  0.0001405679
##               6-9   26 1.651108e-03  0.0036547653
##                60    1 6.350416e-05  0.0001405679
##             60-64    9 5.715374e-04  0.0012651110
##             60-69   15 9.525624e-04  0.0021085184
##             60-79   48 3.048200e-03  0.0067472589
##                63    1 6.350416e-05  0.0001405679
##                65    1 6.350416e-05  0.0001405679
##             65-69    9 5.715374e-04  0.0012651110
##                66    1 6.350416e-05  0.0001405679
##                 7    1 6.350416e-05  0.0001405679
##             70-74   11 6.985458e-04  0.0015462468
##             70-79   16 1.016067e-03  0.0022490863
##                71    2 1.270083e-04  0.0002811358
##             75-79    5 3.175208e-04  0.0007028395
##                 8    1 6.350416e-05  0.0001405679
##                80    1 6.350416e-05  0.0001405679
##             80-84    5 3.175208e-04  0.0007028395
##             80-89    4 2.540166e-04  0.0005622716
##                82    1 6.350416e-05  0.0001405679
##                83    1 6.350416e-05  0.0001405679
##                84    1 6.350416e-05  0.0001405679
##                85    1 6.350416e-05  0.0001405679
##             85-89    6 3.810250e-04  0.0008434074
##                87    3 1.905125e-04  0.0004217037
##                 9    6 3.810250e-04  0.0008434074
##             90-94    6 3.810250e-04  0.0008434074
##              GE50  330 2.095637e-02  0.0463874051
##              GE80   34 2.159141e-03  0.0047793084
##              GE90    3 1.905125e-04  0.0004217037
##              LE10   68 4.318283e-03  0.0095586168
##              LE20  171 1.085921e-02  0.0240371099
##               LE5   17 1.079571e-03  0.0023896542
##              LT50  662 4.203975e-02  0.0930559460
##               n/a 1850 1.174827e-01  0.2600506044
##                PS 2909 1.847336e-01  0.4089120045
##              <NA> 8633 5.482314e-01            NA
mathlea_10 %>% tabyl(HOM_MTHHSPCTPROF)
##  HOM_MTHHSPCTPROF     n      percent valid_percent
##                10     1 6.350416e-05  0.0002473411
##             10-14     1 6.350416e-05  0.0002473411
##             11-19     8 5.080333e-04  0.0019787287
##             15-19     2 1.270083e-04  0.0004946822
##                19     1 6.350416e-05  0.0002473411
##                 2     1 6.350416e-05  0.0002473411
##             20-24     2 1.270083e-04  0.0004946822
##             20-29    12 7.620499e-04  0.0029680930
##             21-39    49 3.111704e-03  0.0121197131
##             25-29     5 3.175208e-04  0.0012367054
##                28     1 6.350416e-05  0.0002473411
##             30-34    12 7.620499e-04  0.0029680930
##             30-39    17 1.079571e-03  0.0042047984
##             35-39     6 3.810250e-04  0.0014840465
##                39     1 6.350416e-05  0.0002473411
##             40-44    10 6.350416e-04  0.0024734108
##             40-49    13 8.255541e-04  0.0032154341
##             40-59    52 3.302216e-03  0.0128617363
##                43     1 6.350416e-05  0.0002473411
##                44     1 6.350416e-05  0.0002473411
##             45-49    13 8.255541e-04  0.0032154341
##                49     1 6.350416e-05  0.0002473411
##             50-54     8 5.080333e-04  0.0019787287
##             50-59    15 9.525624e-04  0.0037101163
##                53     1 6.350416e-05  0.0002473411
##             55-59     3 1.905125e-04  0.0007420233
##               6-9     1 6.350416e-05  0.0002473411
##             60-64     3 1.905125e-04  0.0007420233
##             60-69     6 3.810250e-04  0.0014840465
##             60-79    35 2.222646e-03  0.0086569379
##             65-69     3 1.905125e-04  0.0007420233
##             70-74     2 1.270083e-04  0.0004946822
##             70-79    11 6.985458e-04  0.0027207519
##             75-79     1 6.350416e-05  0.0002473411
##             80-84     2 1.270083e-04  0.0004946822
##             80-89     5 3.175208e-04  0.0012367054
##             90-94     1 6.350416e-05  0.0002473411
##              GE50   191 1.212929e-02  0.0472421469
##              GE80     5 3.175208e-04  0.0012367054
##              GE90     2 1.270083e-04  0.0004946822
##              LE10     4 2.540166e-04  0.0009893643
##              LE20    25 1.587604e-03  0.0061835271
##               LE5     1 6.350416e-05  0.0002473411
##              LT50   241 1.530450e-02  0.0596092011
##               n/a  1819 1.155141e-01  0.4499134306
##                PS  1448 9.195402e-02  0.3581498887
##              <NA> 11704 7.432527e-01            NA
mathlea_10 %>% tabyl(MIG_MTHHSPCTPROF)
##  MIG_MTHHSPCTPROF     n      percent valid_percent
##             10-14     1 6.350416e-05  0.0002954210
##             11-19     6 3.810250e-04  0.0017725258
##             15-19     5 3.175208e-04  0.0014771049
##             20-24     3 1.905125e-04  0.0008862629
##             20-29    11 6.985458e-04  0.0032496307
##             21-39    29 1.841621e-03  0.0085672083
##             25-29    10 6.350416e-04  0.0029542097
##             30-34     4 2.540166e-04  0.0011816839
##             30-39    22 1.397092e-03  0.0064992614
##                34     1 6.350416e-05  0.0002954210
##             35-39    11 6.985458e-04  0.0032496307
##                38     1 6.350416e-05  0.0002954210
##             40-44     7 4.445291e-04  0.0020679468
##             40-49    12 7.620499e-04  0.0035450517
##             40-59    25 1.587604e-03  0.0073855244
##                41     2 1.270083e-04  0.0005908419
##             45-49     4 2.540166e-04  0.0011816839
##             50-54     2 1.270083e-04  0.0005908419
##             50-59     9 5.715374e-04  0.0026587888
##             55-59     5 3.175208e-04  0.0014771049
##                58     1 6.350416e-05  0.0002954210
##               6-9     1 6.350416e-05  0.0002954210
##             60-64     5 3.175208e-04  0.0014771049
##             60-69     5 3.175208e-04  0.0014771049
##             60-79    15 9.525624e-04  0.0044313146
##             65-69     5 3.175208e-04  0.0014771049
##             70-74     2 1.270083e-04  0.0005908419
##             70-79     2 1.270083e-04  0.0005908419
##             75-79     1 6.350416e-05  0.0002954210
##             80-84     1 6.350416e-05  0.0002954210
##             80-89     1 6.350416e-05  0.0002954210
##              GE50   102 6.477424e-03  0.0301329394
##              GE80     6 3.810250e-04  0.0017725258
##              LE10     3 1.905125e-04  0.0008862629
##              LE20    13 8.255541e-04  0.0038404727
##              LT50   141 8.954086e-03  0.0416543575
##               n/a  1963 1.246587e-01  0.5799113737
##                PS   948 6.020194e-02  0.2800590842
##              <NA> 12362 7.850384e-01            NA
mathlea_10 %>% tabyl(CWD_MTHHSPCTPROF)
##  CWD_MTHHSPCTPROF    n      percent valid_percent
##                10    2 1.270083e-04  1.725328e-04
##             10-14  131 8.319045e-03  1.130090e-02
##                11   11 6.985458e-04  9.489303e-04
##             11-19  250 1.587604e-02  2.156660e-02
##                12    3 1.905125e-04  2.587992e-04
##                13    3 1.905125e-04  2.587992e-04
##                14    2 1.270083e-04  1.725328e-04
##                15    5 3.175208e-04  4.313320e-04
##             15-19  153 9.716136e-03  1.319876e-02
##                16    5 3.175208e-04  4.313320e-04
##                17    3 1.905125e-04  2.587992e-04
##                18    2 1.270083e-04  1.725328e-04
##                19    6 3.810250e-04  5.175983e-04
##                 2    1 6.350416e-05  8.626639e-05
##                20    3 1.905125e-04  2.587992e-04
##             20-24  128 8.128532e-03  1.104210e-02
##             20-29  267 1.695561e-02  2.303313e-02
##                21    8 5.080333e-04  6.901311e-04
##             21-39  624 3.962660e-02  5.383023e-02
##                22    6 3.810250e-04  5.175983e-04
##                24    2 1.270083e-04  1.725328e-04
##                25    9 5.715374e-04  7.763975e-04
##             25-29  116 7.366483e-03  1.000690e-02
##                26    4 2.540166e-04  3.450656e-04
##                27    2 1.270083e-04  1.725328e-04
##                28    4 2.540166e-04  3.450656e-04
##                29    1 6.350416e-05  8.626639e-05
##                30    4 2.540166e-04  3.450656e-04
##             30-34  111 7.048962e-03  9.575569e-03
##             30-39  243 1.543151e-02  2.096273e-02
##                32    5 3.175208e-04  4.313320e-04
##                33    3 1.905125e-04  2.587992e-04
##                34    7 4.445291e-04  6.038647e-04
##                35    1 6.350416e-05  8.626639e-05
##             35-39   81 5.143837e-03  6.987578e-03
##                36    3 1.905125e-04  2.587992e-04
##                37    3 1.905125e-04  2.587992e-04
##                38    2 1.270083e-04  1.725328e-04
##                39    1 6.350416e-05  8.626639e-05
##                40    5 3.175208e-04  4.313320e-04
##             40-44   93 5.905887e-03  8.022774e-03
##             40-49  177 1.124024e-02  1.526915e-02
##             40-59  445 2.825935e-02  3.838854e-02
##                41    7 4.445291e-04  6.038647e-04
##                42    1 6.350416e-05  8.626639e-05
##                43    2 1.270083e-04  1.725328e-04
##                44    4 2.540166e-04  3.450656e-04
##                45    3 1.905125e-04  2.587992e-04
##             45-49   74 4.699308e-03  6.383713e-03
##                46    1 6.350416e-05  8.626639e-05
##                47    2 1.270083e-04  1.725328e-04
##                48    1 6.350416e-05  8.626639e-05
##                49    4 2.540166e-04  3.450656e-04
##                50    2 1.270083e-04  1.725328e-04
##             50-54   51 3.238712e-03  4.399586e-03
##             50-59  129 8.192037e-03  1.112836e-02
##                51    2 1.270083e-04  1.725328e-04
##                52    1 6.350416e-05  8.626639e-05
##                53    2 1.270083e-04  1.725328e-04
##                54    1 6.350416e-05  8.626639e-05
##                55    2 1.270083e-04  1.725328e-04
##             55-59   47 2.984695e-03  4.054520e-03
##                57    2 1.270083e-04  1.725328e-04
##                58    1 6.350416e-05  8.626639e-05
##                59    1 6.350416e-05  8.626639e-05
##               6-9   66 4.191275e-03  5.693582e-03
##                60    1 6.350416e-05  8.626639e-05
##             60-64   37 2.349654e-03  3.191856e-03
##             60-69  113 7.175970e-03  9.748102e-03
##             60-79  255 1.619356e-02  2.199793e-02
##                61    1 6.350416e-05  8.626639e-05
##                62    2 1.270083e-04  1.725328e-04
##                63    1 6.350416e-05  8.626639e-05
##                65    2 1.270083e-04  1.725328e-04
##             65-69   39 2.476662e-03  3.364389e-03
##                66    1 6.350416e-05  8.626639e-05
##                67    1 6.350416e-05  8.626639e-05
##                69    2 1.270083e-04  1.725328e-04
##                 7    1 6.350416e-05  8.626639e-05
##             70-74   25 1.587604e-03  2.156660e-03
##             70-79   71 4.508795e-03  6.124914e-03
##                73    1 6.350416e-05  8.626639e-05
##                75    1 6.350416e-05  8.626639e-05
##             75-79   27 1.714612e-03  2.329193e-03
##                76    1 6.350416e-05  8.626639e-05
##                77    2 1.270083e-04  1.725328e-04
##                78    2 1.270083e-04  1.725328e-04
##                80    1 6.350416e-05  8.626639e-05
##             80-84   20 1.270083e-03  1.725328e-03
##             80-89   47 2.984695e-03  4.054520e-03
##                81    2 1.270083e-04  1.725328e-04
##                82    1 6.350416e-05  8.626639e-05
##                84    1 6.350416e-05  8.626639e-05
##             85-89   15 9.525624e-04  1.293996e-03
##                86    1 6.350416e-05  8.626639e-05
##                87    1 6.350416e-05  8.626639e-05
##                 9    1 6.350416e-05  8.626639e-05
##             90-94   11 6.985458e-04  9.489303e-04
##              GE50 1028 6.528228e-02  8.868185e-02
##              GE80   96 6.096399e-03  8.281573e-03
##              GE90   27 1.714612e-03  2.329193e-03
##              GE95    1 6.350416e-05  8.626639e-05
##              LE10  206 1.308186e-02  1.777088e-02
##              LE20  750 4.762812e-02  6.469979e-02
##               LE5   27 1.714612e-03  2.329193e-03
##              LT50 2342 1.487267e-01  2.020359e-01
##               n/a  165 1.047819e-02  1.423395e-02
##                PS 2922 1.855592e-01  2.520704e-01
##              <NA> 4155 2.638598e-01            NA

Data Visualization 2

Ksenia

Research Question:

What drives current expenditure on education? How is funding allocated by state and how do the funding allocations correlate with eh student performance (graduation rates and/or test scores)? Does cross-state variation in expenditure explain the cross-state variation in education outcomes? More specifically, I might look at the following:

  • What percentage of expenditure accounts for instruction/ textbooks/ technology-related equipment & services/ instructional equipment?
  • What are the gross amounts spent on those categories?
  • How do those percentages/ gross amounts correlate with the diversity of the student population/portion of white students in school?
  • Does increased spending on any of the categories correlate with increased students’ performance?
  • Can higher teachers’ salaries result in better test scores?
  • What amount of funding is spent on special education programs and how that correlates with performance of students with disabilities?

Datasets:

  • EDFacts_rla_achievement_lea_2010_2019
  • EDFacts_math_achievement_lea_2010_2019
  • NCES_CCD_fiscal_district_2010
colnames(fiscal2010)
##   [1] "A07"                   "A08"                   "A09"                  
##   [4] "A11"                   "A13"                   "A15"                  
##   [7] "A20"                   "A40"                   "AGCHRT"               
##  [10] "B10"                   "B11"                   "B12"                  
##  [13] "B13"                   "C01"                   "C04"                  
##  [16] "C05"                   "C06"                   "C07"                  
##  [19] "C08"                   "C09"                   "C10"                  
##  [22] "C11"                   "C12"                   "C13"                  
##  [25] "C14"                   "C15"                   "C16"                  
##  [28] "C17"                   "C19"                   "C20"                  
##  [31] "C24"                   "C25"                   "C35"                  
##  [34] "C36"                   "C38"                   "C39"                  
##  [37] "CBSA"                  "CCDNF"                 "CENFILE"              
##  [40] "CENSUSID"              "CONUM"                 "CSA"                  
##  [43] "D11"                   "D23"                   "E07"                  
##  [46] "E08"                   "E09"                   "E11"                  
##  [49] "E13"                   "E17"                   "F12"                  
##  [52] "FIPST"                 "FL_19H"                "FL_21F"               
##  [55] "FL_31F"                "FL_41F"                "FL_61V"               
##  [58] "FL_66V"                "FL_A07"                "FL_A08"               
##  [61] "FL_A09"                "FL_A11"                "FL_A13"               
##  [64] "FL_A15"                "FL_A20"                "FL_A40"               
##  [67] "FL_B10"                "FL_B11"                "FL_B12"               
##  [70] "FL_B13"                "FL_C01"                "FL_C04"               
##  [73] "FL_C05"                "FL_C06"                "FL_C07"               
##  [76] "FL_C08"                "FL_C09"                "FL_C10"               
##  [79] "FL_C11"                "FL_C12"                "FL_C13"               
##  [82] "FL_C14"                "FL_C15"                "FL_C16"               
##  [85] "FL_C17"                "FL_C19"                "FL_C20"               
##  [88] "FL_C24"                "FL_C25"                "FL_C35"               
##  [91] "FL_C36"                "FL_C38"                "FL_C39"               
##  [94] "FL_D11"                "FL_D23"                "FL_E07"               
##  [97] "FL_E08"                "FL_E09"                "FL_E11"               
## [100] "FL_E13"                "FL_E17"                "FL_F12"               
## [103] "FL_G15"                "FL_HE1"                "FL_HE2"               
## [106] "FL_HR1"                "FL_I86"                "FL_K09"               
## [109] "FL_K10"                "FL_K11"                "FL_L12"               
## [112] "FL_M12"                "FL_MEMBERSCH"          "FL_Q11"               
## [115] "FL_T02"                "FL_T06"                "FL_T09"               
## [118] "FL_T15"                "FL_T40"                "FL_T99"               
## [121] "FL_U11"                "FL_U22"                "FL_U30"               
## [124] "FL_U50"                "FL_U97"                "FL_V10"               
## [127] "FL_V11"                "FL_V12"                "FL_V13"               
## [130] "FL_V14"                "FL_V15"                "FL_V16"               
## [133] "FL_V17"                "FL_V18"                "FL_V21"               
## [136] "FL_V22"                "FL_V23"                "FL_V24"               
## [139] "FL_V29"                "FL_V30"                "FL_V32"               
## [142] "FL_V33"                "FL_V37"                "FL_V38"               
## [145] "FL_V40"                "FL_V45"                "FL_V60"               
## [148] "FL_V65"                "FL_V70"                "FL_V75"               
## [151] "FL_V80"                "FL_V85"                "FL_V90"               
## [154] "FL_V91"                "FL_V92"                "FL_V93"               
## [157] "FL_W01"                "FL_W31"                "FL_W61"               
## [160] "FL_Z32"                "FL_Z33"                "FL_Z34"               
## [163] "FL_Z35"                "FL_Z36"                "FL_Z37"               
## [166] "FL_Z38"                "FILEURL"               "G15"                  
## [169] "GSHI"                  "GSLO"                  "HE1"                  
## [172] "HE2"                   "HR1"                   "I86"                  
## [175] "K09"                   "K10"                   "K11"                  
## [178] "L12"                   "LEAID"                 "M12"                  
## [181] "MEMBERSCH"             "NAME"                  "Q11"                  
## [184] "SCHLEV"                "STABBR"                "STNAME"               
## [187] "T02"                   "T06"                   "T09"                  
## [190] "T15"                   "T40"                   "T99"                  
## [193] "TCAPOUT"               "TCURELSC"              "TCURINST"             
## [196] "TCUROTH"               "TCURSSVC"              "TFEDREV"              
## [199] "TLOCREV"               "TNONELSE"              "TOTALEXP"             
## [202] "TOTALREV"              "TSTREV"                "U11"                  
## [205] "U22"                   "U30"                   "U50"                  
## [208] "U97"                   "V10"                   "V11"                  
## [211] "V12"                   "V13"                   "V14"                  
## [214] "V15"                   "V16"                   "V17"                  
## [217] "V18"                   "V21"                   "V22"                  
## [220] "V23"                   "V24"                   "V29"                  
## [223] "V30"                   "V32"                   "V33"                  
## [226] "V37"                   "V38"                   "V40"                  
## [229] "V45"                   "V60"                   "V65"                  
## [232] "V70"                   "V75"                   "V80"                  
## [235] "V85"                   "V90"                   "V91"                  
## [238] "V92"                   "V93"                   "W01"                  
## [241] "W31"                   "W61"                   "WEIGHT"               
## [244] "Z32"                   "Z33"                   "Z34"                  
## [247] "Z35"                   "Z36"                   "Z37"                  
## [250] "Z38"                   "_19H"                  "_21F"                 
## [253] "_31F"                  "_41F"                  "_61V"                 
## [256] "_66V"                  "_YEAR"                 "YEAR"                 
## [259] "PIPELINE"              "DL_INGESTION_DATETIME"
skim(fiscal2010)
Data summary
Name fiscal2010
Number of rows 18247
Number of columns 260
_______________________
Column type frequency:
character 129
numeric 130
POSIXct 1
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
AGCHRT 0 1 1 1 0 4 0
CBSA 0 1 1 5 0 939 0
CENSUSID 0 1 1 14 0 14842 0
CONUM 0 1 5 5 0 3129 0
CSA 0 1 1 3 0 125 0
FIPST 0 1 2 2 0 51 0
FL_19H 0 1 1 1 0 4 0
FL_21F 0 1 1 1 0 4 0
FL_31F 0 1 1 1 0 4 0
FL_41F 0 1 1 1 0 4 0
FL_61V 0 1 1 1 0 3 0
FL_66V 0 1 1 1 0 4 0
FL_A07 0 1 1 1 0 4 0
FL_A08 0 1 1 1 0 3 0
FL_A09 0 1 1 1 0 3 0
FL_A11 0 1 1 1 0 3 0
FL_A13 0 1 1 1 0 3 0
FL_A15 0 1 1 1 0 4 0
FL_A20 0 1 1 1 0 4 0
FL_A40 0 1 1 1 0 4 0
FL_B10 0 1 1 1 0 3 0
FL_B11 0 1 1 1 0 3 0
FL_B12 0 1 1 1 0 4 0
FL_B13 0 1 1 1 0 3 0
FL_C01 0 1 1 1 0 4 0
FL_C04 0 1 1 1 0 4 0
FL_C05 0 1 1 1 0 3 0
FL_C06 0 1 1 1 0 3 0
FL_C07 0 1 1 1 0 4 0
FL_C08 0 1 1 1 0 3 0
FL_C09 0 1 1 1 0 4 0
FL_C10 0 1 1 1 0 3 0
FL_C11 0 1 1 1 0 3 0
FL_C12 0 1 1 1 0 3 0
FL_C13 0 1 1 1 0 4 0
FL_C14 0 1 1 1 0 4 0
FL_C15 0 1 1 1 0 3 0
FL_C16 0 1 1 1 0 3 0
FL_C17 0 1 1 1 0 3 0
FL_C19 0 1 1 1 0 3 0
FL_C20 0 1 1 1 0 4 0
FL_C24 0 1 1 1 0 3 0
FL_C25 0 1 1 1 0 4 0
FL_C35 0 1 1 1 0 3 0
FL_C36 0 1 1 1 0 4 0
FL_C38 0 1 1 1 0 4 0
FL_C39 0 1 1 1 0 4 0
FL_D11 0 1 1 1 0 4 0
FL_D23 0 1 1 1 0 4 0
FL_E07 0 1 1 1 0 5 0
FL_E08 0 1 1 1 0 5 0
FL_E09 0 1 1 1 0 5 0
FL_E11 0 1 1 1 0 4 0
FL_E13 0 1 1 1 0 5 0
FL_E17 0 1 1 1 0 5 0
FL_F12 0 1 1 1 0 4 0
FL_G15 0 1 1 1 0 4 0
FL_HE1 0 1 1 1 0 4 0
FL_HE2 0 1 1 1 0 4 0
FL_HR1 0 1 1 1 0 4 0
FL_I86 0 1 1 1 0 4 0
FL_K09 0 1 1 1 0 5 0
FL_K10 0 1 1 1 0 4 0
FL_K11 0 1 1 1 0 4 0
FL_L12 0 1 1 1 0 3 0
FL_M12 0 1 1 1 0 3 0
FL_MEMBERSCH 0 1 1 1 0 3 0
FL_Q11 0 1 1 1 0 4 0
FL_T02 0 1 1 1 0 4 0
FL_T06 0 1 1 1 0 4 0
FL_T09 0 1 1 1 0 3 0
FL_T15 0 1 1 1 0 3 0
FL_T40 0 1 1 1 0 3 0
FL_T99 0 1 1 1 0 4 0
FL_U11 0 1 1 1 0 3 0
FL_U22 0 1 1 1 0 4 0
FL_U30 0 1 1 1 0 3 0
FL_U50 0 1 1 1 0 4 0
FL_U97 0 1 1 1 0 4 0
FL_V10 0 1 1 1 0 5 0
FL_V11 0 1 1 1 0 4 0
FL_V12 0 1 1 1 0 4 0
FL_V13 0 1 1 1 0 3 0
FL_V14 0 1 1 1 0 4 0
FL_V15 0 1 1 1 0 3 0
FL_V16 0 1 1 1 0 4 0
FL_V17 0 1 1 1 0 3 0
FL_V18 0 1 1 1 0 4 0
FL_V21 0 1 1 1 0 3 0
FL_V22 0 1 1 1 0 4 0
FL_V23 0 1 1 1 0 4 0
FL_V24 0 1 1 1 0 5 0
FL_V29 0 1 1 1 0 3 0
FL_V30 0 1 1 1 0 5 0
FL_V32 0 1 1 1 0 3 0
FL_V33 0 1 1 1 0 3 0
FL_V37 0 1 1 1 0 4 0
FL_V38 0 1 1 1 0 5 0
FL_V40 0 1 1 1 0 4 0
FL_V45 0 1 1 1 0 5 0
FL_V60 0 1 1 1 0 4 0
FL_V65 0 1 1 1 0 4 0
FL_V70 0 1 1 1 0 3 0
FL_V75 0 1 1 1 0 3 0
FL_V80 0 1 1 1 0 4 0
FL_V85 0 1 1 1 0 4 0
FL_V90 0 1 1 1 0 5 0
FL_V91 0 1 1 1 0 4 0
FL_V92 0 1 1 1 0 3 0
FL_V93 0 1 1 1 0 4 0
FL_W01 0 1 1 1 0 4 0
FL_W31 0 1 1 1 0 4 0
FL_W61 0 1 1 1 0 4 0
FL_Z32 0 1 1 1 0 4 0
FL_Z33 0 1 1 1 0 4 0
FL_Z34 0 1 1 1 0 5 0
FL_Z35 0 1 1 1 0 4 0
FL_Z36 0 1 1 1 0 3 0
FL_Z37 0 1 1 1 0 4 0
FL_Z38 0 1 1 1 0 3 0
FILEURL 0 1 48 48 0 1 0
GSHI 0 1 1 2 0 16 0
GSLO 0 1 1 2 0 16 0
LEAID 0 1 7 7 0 18247 0
NAME 0 1 3 60 0 17717 0
SCHLEV 0 1 1 2 0 7 0
STABBR 0 1 2 2 0 51 0
STNAME 0 1 4 20 0 51 0
PIPELINE 0 1 29 29 0 1 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
A07 0 1 62321.43 402543.33 -2 0 0 9000.0 20229000 ▇▁▁▁▁
A08 0 1 5939.67 128277.93 -2 0 0 0.0 15564000 ▇▁▁▁▁
A09 0 1 364123.59 1144107.53 -2 3000 79000 319000.0 47205000 ▇▁▁▁▁
A11 0 1 10957.26 85439.61 -2 0 0 0.0 3510000 ▇▁▁▁▁
A13 0 1 211909.26 1823605.27 -2 0 8000 97000.0 171949000 ▇▁▁▁▁
A15 0 1 8501.85 191879.04 -2 0 0 0.0 14939000 ▇▁▁▁▁
A20 0 1 91805.46 808818.77 -2 0 0 3000.0 49072000 ▇▁▁▁▁
A40 0 1 41828.48 293969.27 -2 0 0 9000.0 19348000 ▇▁▁▁▁
B10 0 1 68834.01 917277.92 -2 0 0 0.0 59398000 ▇▁▁▁▁
B11 0 1 19931.51 259028.50 -2 0 0 0.0 28169000 ▇▁▁▁▁
B12 0 1 5454.66 56806.67 -2 0 0 0.0 3111000 ▇▁▁▁▁
B13 0 1 162690.86 2006666.21 -2 0 0 24000.0 213069000 ▇▁▁▁▁
C01 0 1 9904463.16 56726682.92 -2 481000 2407000 7717500.0 6016802000 ▇▁▁▁▁
C04 0 1 177204.10 1791292.67 -2 0 0 0.0 139157000 ▇▁▁▁▁
C05 0 1 918167.93 12489751.14 -2 0 0 331000.0 1445472000 ▇▁▁▁▁
C06 0 1 255034.54 3212965.61 -2 0 0 47000.0 262095000 ▇▁▁▁▁
C07 0 1 33335.24 848165.31 -2 0 0 0.0 52887000 ▇▁▁▁▁
C08 0 1 23623.02 667675.15 -2 0 0 0.0 68096000 ▇▁▁▁▁
C09 0 1 43071.09 351589.50 -2 0 0 0.0 16140000 ▇▁▁▁▁
C10 0 1 28732.95 385107.93 -2 0 2000 12000.0 42871000 ▇▁▁▁▁
C11 0 1 340673.71 3307919.59 -2 0 0 0.0 289060000 ▇▁▁▁▁
C12 0 1 219562.73 1235888.02 -2 0 0 68000.0 76286000 ▇▁▁▁▁
C13 0 1 1467624.72 15568842.66 -2 1000 76000 506000.0 1684314000 ▇▁▁▁▁
C14 0 1 928407.42 8605443.22 -2 23000 146000 508000.0 777993000 ▇▁▁▁▁
C15 0 1 783204.43 4229045.40 -2 0 69000 473000.0 257678000 ▇▁▁▁▁
C16 0 1 91089.01 662844.07 -2 0 0 43000.0 58822000 ▇▁▁▁▁
C17 0 1 13732.90 128064.08 -2 0 0 4500.0 10177000 ▇▁▁▁▁
C19 0 1 34663.02 223558.75 -2 0 0 2000.0 15750000 ▇▁▁▁▁
C20 0 1 1216737.06 6249021.27 -2 1000 178000 753000.0 394540000 ▇▁▁▁▁
C24 0 1 198884.54 5846320.83 -2 0 0 0.0 729737000 ▇▁▁▁▁
C25 0 1 658136.53 4121046.74 -2 11000 118000 399500.0 321606000 ▇▁▁▁▁
C35 0 1 111686.04 1947391.23 -2 0 0 0.0 208110000 ▇▁▁▁▁
C36 0 1 141945.32 4773984.42 -2 0 0 0.0 637078000 ▇▁▁▁▁
C38 0 1 631631.02 3267240.10 -2 0 0 161000.0 144110000 ▇▁▁▁▁
C39 0 1 31920.66 320989.51 -2 0 0 0.0 19306000 ▇▁▁▁▁
CCDNF 0 1 1.00 0.04 0 1 1 1.0 1 ▁▁▁▁▇
CENFILE 0 1 0.81 0.39 0 1 1 1.0 1 ▂▁▁▁▇
D11 0 1 603561.64 3194163.22 -2 0 0 208000.0 201357000 ▇▁▁▁▁
D23 0 1 442007.85 3606971.99 -2 0 0 46000.0 340982000 ▇▁▁▁▁
E07 0 1 1366785.35 6893020.79 -2 34000 227000 952000.0 479925000 ▇▁▁▁▁
E08 0 1 550224.54 1818703.71 -2 101000 283000 593000.0 144467000 ▇▁▁▁▁
E09 0 1 1559184.15 7539644.20 -2 102000 381000 1206000.0 528651000 ▇▁▁▁▁
E11 0 1 1071170.56 5492398.66 -2 50000 261000 830000.0 448671000 ▇▁▁▁▁
E13 0 1 17327081.34 127893156.30 -2 1171000 4198000 13455500.0 14936045000 ▇▁▁▁▁
E17 0 1 1578756.96 6042710.01 -2 46000 286000 1203000.0 333309000 ▇▁▁▁▁
F12 0 1 2583185.58 27822230.17 -2 0 37000 675000.0 3044559000 ▇▁▁▁▁
G15 0 1 181778.44 1733083.39 -2 0 0 0.0 106072000 ▇▁▁▁▁
HE1 0 1 1388857.91 10400685.61 -2 55000 289000 968000.0 1050214000 ▇▁▁▁▁
HE2 0 1 64137.90 490090.60 -2 0 0 24000.0 36903000 ▇▁▁▁▁
HR1 0 1 218646.58 2227187.59 -2 0 26000 115000.0 232282000 ▇▁▁▁▁
I86 0 1 971108.03 6437069.74 -2 0 64000 529000.0 439446000 ▇▁▁▁▁
K09 0 1 137397.72 665773.23 -2 0 18000 95000.0 48388000 ▇▁▁▁▁
K10 0 1 353665.65 1589523.52 -2 2000 65000 242000.0 61852000 ▇▁▁▁▁
K11 0 1 23581.48 246991.88 -2 0 0 0.0 12401000 ▇▁▁▁▁
L12 0 1 80185.74 1349349.83 -2 0 0 0.0 111938000 ▇▁▁▁▁
M12 0 1 11888.16 391698.39 -2 0 0 0.0 49058000 ▇▁▁▁▁
MEMBERSCH 0 1 2690.61 12815.67 -9 167 641 2083.5 1014020 ▇▁▁▁▁
Q11 0 1 702918.96 7421719.40 -2 0 33000 283000.0 634364000 ▇▁▁▁▁
T02 0 1 2639423.57 73594947.69 -2 -2 -2 -2.0 9073697000 ▇▁▁▁▁
T06 0 1 9271963.74 39717090.27 -2 -2 1187000 5982000.0 1818529000 ▇▁▁▁▁
T09 0 1 203461.80 3394275.08 -2 -2 0 0.0 161332000 ▇▁▁▁▁
T15 0 1 19918.86 255405.26 -2 -2 0 0.0 20748000 ▇▁▁▁▁
T40 0 1 99695.20 1287236.16 -2 -2 0 0.0 110682000 ▇▁▁▁▁
T99 0 1 74279.41 909702.46 -2 -2 0 0.0 58982000 ▇▁▁▁▁
TCAPOUT 0 1 3279609.48 29521711.94 -2 35000 285000 1333000.0 3151607000 ▇▁▁▁▁
TCURELSC 0 1 28436776.91 179053170.29 -2 2109000 7146000 22648500.0 19453219000 ▇▁▁▁▁
TCURINST 0 1 17327081.34 127893156.30 -2 1171000 4198000 13455500.0 14936045000 ▇▁▁▁▁
TCUROTH 0 1 1140166.50 5600778.27 -2 55500 283000 874000.0 448671000 ▇▁▁▁▁
TCURSSVC 0 1 9969528.76 48510265.33 -2 776000 2588000 8035000.0 4068503000 ▇▁▁▁▁
TFEDREV 0 1 4124828.42 25252763.77 -2 261000 899000 2680000.0 2047926000 ▇▁▁▁▁
TLOCREV 0 1 15170253.20 97409268.54 -2 499000 3031000 10780000.0 10600597000 ▇▁▁▁▁
TNONELSE 0 1 363810.50 3018302.79 -2 0 3000 109000.0 175673000 ▇▁▁▁▁
TOTALEXP 0 1 34140053.39 224183538.28 -2 2408000 8445000 26676000.0 24597709000 ▇▁▁▁▁
TOTALREV 0 1 33481814.83 199406458.00 -2 2434500 8493000 26767000.0 21023695000 ▇▁▁▁▁
TSTREV 0 1 14186732.90 85066587.68 -2 945000 3617000 11322500.0 8375172000 ▇▁▁▁▁
U11 0 1 20131.32 332556.14 -2 0 0 0.0 28720000 ▇▁▁▁▁
U22 0 1 108350.53 735676.31 -2 1000 12000 56000.0 73023000 ▇▁▁▁▁
U30 0 1 19000.78 222943.67 -2 0 0 0.0 19854000 ▇▁▁▁▁
U50 0 1 52941.97 586124.91 -2 0 0 13000.0 48929000 ▇▁▁▁▁
U97 0 1 619238.19 11118869.62 -2 6000 52000 244000.0 1421630000 ▇▁▁▁▁
V10 0 1 3998687.90 38204603.80 -2 186000 870000 3139000.0 4756409000 ▇▁▁▁▁
V11 0 1 1053954.52 4087112.12 -2 15000 176000 789500.0 243582000 ▇▁▁▁▁
V12 0 1 336373.22 1395789.52 -2 2000 51000 247000.0 83188000 ▇▁▁▁▁
V13 0 1 806004.12 4234643.26 -2 10000 118000 543000.0 321611000 ▇▁▁▁▁
V14 0 1 263026.59 1390817.64 -2 2000 37000 186000.0 108630000 ▇▁▁▁▁
V15 0 1 239975.18 825385.53 -2 34000 142000 269000.0 82474000 ▇▁▁▁▁
V16 0 1 92164.15 323598.91 -2 8000 42000 99000.0 25761000 ▇▁▁▁▁
V17 0 1 1107942.03 5242875.32 -2 63500 257000 835000.0 341063000 ▇▁▁▁▁
V18 0 1 370250.30 2069478.16 -2 15000 79000 289000.0 160978000 ▇▁▁▁▁
V21 0 1 978273.54 6346261.99 -2 31000 184000 681500.0 661475000 ▇▁▁▁▁
V22 0 1 388822.12 3028190.56 -2 8000 68000 276000.0 327418000 ▇▁▁▁▁
V23 0 1 416869.36 1827291.51 -2 0 46000 272500.0 68954000 ▇▁▁▁▁
V24 0 1 167106.71 771339.05 -2 0 12000 97000.0 34626000 ▇▁▁▁▁
V29 0 1 357825.95 2204270.87 -2 0 69000 259000.0 221534000 ▇▁▁▁▁
V30 0 1 131409.06 743125.50 -2 0 20000 89000.0 69482000 ▇▁▁▁▁
V32 0 1 4498.39 71129.59 -2 0 0 0.0 4338000 ▇▁▁▁▁
V33 0 1 2696.60 12832.19 -9 165 643 2092.0 1014020 ▇▁▁▁▁
V37 0 1 446569.69 2413022.59 -2 0 87000 315000.0 237834000 ▇▁▁▁▁
V38 0 1 187349.27 1238275.08 -2 0 29000 122000.0 117724000 ▇▁▁▁▁
V40 0 1 2712894.19 16354709.32 -2 187000 690000 2114500.0 1661029000 ▇▁▁▁▁
V45 0 1 1219745.94 8586494.05 -2 35000 269000 978500.0 1024981000 ▇▁▁▁▁
V60 0 1 62758.66 644426.67 -2 0 0 0.0 31334000 ▇▁▁▁▁
V65 0 1 6236.98 120315.98 -2 0 0 0.0 9815000 ▇▁▁▁▁
V70 0 1 201312.99 1906604.78 -2 0 0 43000.0 118338000 ▇▁▁▁▁
V75 0 1 110767.97 1647700.62 -2 0 0 0.0 148516000 ▇▁▁▁▁
V80 0 1 51729.23 615328.22 -2 0 0 0.0 40513000 ▇▁▁▁▁
V85 0 1 248.65 33609.35 -2 0 0 0.0 4540000 ▇▁▁▁▁
V90 0 1 981687.90 6017663.61 -2 43000 202000 676000.0 591836000 ▇▁▁▁▁
V91 0 1 180739.64 5702851.48 -2 0 0 0.0 738402000 ▇▁▁▁▁
V92 0 1 113014.75 3320447.49 -2 0 0 0.0 383908000 ▇▁▁▁▁
V93 0 1 132874.73 1178546.64 -2 0 8000 70000.0 115987000 ▇▁▁▁▁
W01 0 1 864911.54 8526711.15 -2 0 0 182000.0 701763000 ▇▁▁▁▁
W31 0 1 2592562.11 31381207.74 -2 0 0 132000.0 3650875000 ▇▁▁▁▁
W61 0 1 5791300.75 25187070.39 -2 79000 1284000 4379500.0 2178242000 ▇▁▁▁▁
WEIGHT 0 1 1.00 0.00 1 1 1 1.0 1 ▁▁▇▁▁
Z32 0 1 17275789.18 99544925.43 -2 1078000 4110000 13449000.0 10254583000 ▇▁▁▁▁
Z33 0 1 11689210.40 76409484.15 -2 719000 2755000 9035000.0 8613741000 ▇▁▁▁▁
Z34 0 1 6003717.44 46330908.53 -2 284000 1321000 4773000.0 5409968000 ▇▁▁▁▁
Z35 0 1 5979280.17 39123700.39 -2 0 497000 4110500.0 4011575000 ▇▁▁▁▁
Z36 0 1 1360212.70 14071446.23 -2 0 41000 776000.0 1713079000 ▇▁▁▁▁
Z37 0 1 234278.74 3988673.40 -2 0 0 89000.0 524807000 ▇▁▁▁▁
Z38 0 1 296060.84 1767268.94 -2 0 0 111000.0 78518000 ▇▁▁▁▁
_19H 0 1 21207890.62 143384108.88 -2 0 1330000 12291500.0 11615909000 ▇▁▁▁▁
_21F 0 1 2646400.13 39506333.52 -2 0 0 0.0 4342818000 ▇▁▁▁▁
_31F 0 1 1937930.41 16398282.10 -2 0 138000 925000.0 1210275000 ▇▁▁▁▁
_41F 0 1 21921404.30 163411365.20 -2 0 1347000 12785500.0 12642529000 ▇▁▁▁▁
_61V 0 1 502922.79 6044542.90 -2 0 0 0.0 625105000 ▇▁▁▁▁
_66V 0 1 437682.37 3431237.57 -2 0 0 0.0 230000000 ▇▁▁▁▁
_YEAR 0 1 10.00 0.00 10 10 10 10.0 10 ▁▁▇▁▁
YEAR 0 1 2010.00 0.00 2010 2010 2010 2010.0 2010 ▁▁▇▁▁

Variable type: POSIXct

skim_variable n_missing complete_rate min max median n_unique
DL_INGESTION_DATETIME 0 1 2021-09-02 13:02:24 2021-09-02 13:02:24 2021-09-02 13:02:24 1
# Total spendings by state
total <-fiscal2010 %>% 
  group_by(STNAME) %>%
  summarise(TOTALEXP = sum(TOTALEXP))

total  %>%
  ggplot(aes(TOTALEXP, STNAME)) +
  geom_col()

#in descending order
total  %>%
  ggplot(aes(TOTALEXP, fct_reorder(STNAME, TOTALEXP))) +
  geom_col() 

#spending on instruction by state 
instruction <- fiscal2010 %>%
  group_by(STNAME) %>%
      summarise(E13 = sum(E13))

instruction  %>%
  ggplot(aes(E13, STNAME)) +
  geom_col()

instruction  %>%
  ggplot(aes(E13, fct_reorder(STNAME, E13))) +
  geom_col()

#New York spends more on instruction, while CA spends the most total

#spending on textbooks by state 
textbooks <- fiscal2010 %>%
  group_by(STNAME) %>%
      summarise(V93 = sum(V93))

textbooks  %>%
  ggplot(aes(V93, fct_reorder(STNAME, V93))) +
  geom_col()

#spending on Special Ed teachers
SpEd <- fiscal2010 %>%
  group_by(STNAME) %>%
      summarise(Z36 = sum(Z36))

SpEd  %>%
  ggplot(aes(Z36, fct_reorder(STNAME, Z36))) +
  geom_col()

#next step - figure out how to show those spendings per state as a proportion of the total spending if that state

Data Visualization 3

Amy

Research Question:

What is the relationship between the local revenue of local education agencies and students’ literacy outcomes on statewide assessments in 2010? Additional areas of exploration included (a) average total local revenue and local revenue property taxes by state, (b) the relationship between outcomes and total local revenue vs. local revenue from property taxes, (c) the relationship between both types of funding and outcomes by state, and (d) relationship between both types of revenue and outcomes of student subgroups (e.g., race/ethnicity, disability status, language proficiency, SES).

Datasets:

  • EDFacts_rla_achievement_lea_2010_2019
  • NCES_CCD_fiscal_district_2010

Variables:

EDFacts_rla_achievement_lea_2010_2019

  • YEAR
  • STNAM
  • FIPST
  • LEAID
  • ALL_RLA00PCTPROF = Percentage of all students who scored at or above their state’s proficiency level on Reading/Language Arts.
  • MAM_RLA03PCTPROF = Percentage of Native American students that scored at or above proficient
  • MAS_RLA00PCTPROF = Percentage of Asian/Pacific Islander students that scored at or above proficient
  • MBL_RLA00PCTPROF = Percentage of Black students that scored at or above proficient
  • MHI_RLA00PCTPROF = Percentage of Hispanic students that scored at or above proficient
  • MTR_RLA00PCTPROF = Percentage of students with Two or More Races that scored at or above proficient
  • MWH_RLA03PCTPROF = Percentage of White students that scored at or above proficient
  • CWD_RLA00PCTPROF = Percentage of children with disabilities that scored at or above proficient
  • ECD_RLA00PCTPROF = Percentage of economically disadvantaged students that scored at or above proficient
  • LEP_RLA00PCTPROF = Percentage of limited English proficient students that scored at or above proficient

NCES_CCD_fiscal_district_2010:

  • NAME
  • STABBR
  • CENSUSID
  • V33 = LEA fall membership (i.e., number of students)
  • TOTALREV = LEA total revenue
  • TFEDREV = LEA total federal revenue
  • TSTREV = LEA total state revenue
  • TLOCREV = LEA total local revenue
  • T06 = LEA local revenue from property taxes

Cleaning and Wrangling

# Selected columns of interest. Filtered so that rows with suppressed values (e.g., -9, -2) for key variables of interest weren't included. 
viz3_fiscal2010 <- fiscal2010 %>% 
  select(LEAID, NAME, STABBR, CENSUSID, V33,
         TOTALREV, TFEDREV, TSTREV, TLOCREV, T06) %>% 
  filter(V33 > 0, TLOCREV > 0, T06 > 0) %>%
  rename_with(tolower) %>% 
  rename(totalstu = v33, tlocrevtaxes = t06) %>% 
  mutate(locrev_stu = tlocrev / totalstu,
         locrevtaxes_stu = tlocrevtaxes / totalstu)
# Narrowed the RLA 2010 file down to variables of interest. Selected percent proficient variables across all grades (00) for race ethnicity, disability, English Language Learner status, and economically disadvantaged subgroups. Transformed variable names to be lowercase, transformed state names to be title case, and replaced the suppressed values (e.g., PS, n/a, etc.) with NA.
viz3_rlalea00 <- rlalea_10 %>% 
  select(YEAR, 
         STNAM, 
         FIPST,
         LEAID, 
         ALL_RLA00PCTPROF, 
         MAM_RLA00PCTPROF,
         MAS_RLA00PCTPROF,
         MBL_RLA00PCTPROF,
         MHI_RLA00PCTPROF,
         MTR_RLA00PCTPROF, 
         MWH_RLA00PCTPROF,
         CWD_RLA00PCTPROF,
         ECD_RLA00PCTPROF,
         LEP_RLA00PCTPROF) %>% 
  rename_with(tolower)  %>% 
  mutate(stnam = str_to_title(stnam), 
         (across(all_rla00pctprof:lep_rla00pctprof, 
                 ~replace(., . %in% c("PS", 
                                      "n/a",    
                                      "LT50",   
                                      "LE5",    
                                      "LE20",   
                                      "LE10",   
                                      "GE99",   
                                      "GE95",   
                                      "GE90",   
                                      "GE80",   
                                      "GE50"), NA))))

# Next step was cleaning the percentage columns to change percentage ranges to average percentages. I used the method Daniel used on the course data webpage and applied it to all subgroups.

# All = across all students
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(all_rla00pctprof, c("all_lower", "all_upper"), sep = "-") %>% 
  mutate(
    all_upper = ifelse(is.na(all_upper), all_lower, all_upper),
    all_lower = as.numeric(all_lower),
    all_upper = as.numeric(all_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_all = mean(c(all_lower, all_upper))) %>% 
  ungroup()

# mam = American Indian/Alaska Native
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(mam_rla00pctprof, c("mam_lower", "mam_upper"), sep = "-") %>% 
  mutate(
    mam_upper = ifelse(is.na(mam_upper), mam_lower, mam_upper),
    mam_lower = as.numeric(mam_lower),
    mam_upper = as.numeric(mam_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_mam = mean(c(mam_lower, mam_upper))) %>% 
  ungroup()

# mas = Asian/Pacific Islander
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(mas_rla00pctprof, c("mas_lower", "mas_upper"), sep = "-") %>% 
  mutate(
    mas_upper = ifelse(is.na(mas_upper), mas_lower, mas_upper),
    mas_lower = as.numeric(mas_lower),
    mas_upper = as.numeric(mas_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_mas = mean(c(mas_lower, mas_upper))) %>% 
  ungroup()

# mbl = Black
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(mbl_rla00pctprof, c("mbl_lower", "mbl_upper"), sep = "-") %>% 
  mutate(
    mbl_upper = ifelse(is.na(mbl_upper), mbl_lower, mbl_upper),
    mbl_lower = as.numeric(mbl_lower),
    mbl_upper = as.numeric(mbl_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_mbl = mean(c(mbl_lower, mbl_upper))) %>% 
  ungroup()

# mhi = Hispanic/Latino
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(mhi_rla00pctprof, c("mhi_lower", "mhi_upper"), sep = "-") %>% 
  mutate(
    mhi_upper = ifelse(is.na(mhi_upper), mhi_lower, mhi_upper),
    mhi_lower = as.numeric(mhi_lower),
    mhi_upper = as.numeric(mhi_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_mhi = mean(c(mhi_lower, mhi_upper))) %>% 
  ungroup()

# mtr = Multiracial
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(mtr_rla00pctprof, c("mtr_lower", "mtr_upper"), sep = "-") %>% 
  mutate(
    mtr_upper = ifelse(is.na(mtr_upper), mtr_lower, mtr_upper),
    mtr_lower = as.numeric(mtr_lower),
    mtr_upper = as.numeric(mtr_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_mtr = mean(c(mtr_lower, mtr_upper))) %>% 
  ungroup()

# mwh = White
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(mwh_rla00pctprof, c("mwh_lower", "mwh_upper"), sep = "-") %>% 
  mutate(
    mwh_upper = ifelse(is.na(mwh_upper), mwh_lower, mwh_upper),
    mwh_lower = as.numeric(mwh_lower),
    mwh_upper = as.numeric(mwh_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_mwh = mean(c(mwh_lower, mwh_upper))) %>% 
  ungroup()

# cwd = children with disabilities
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(cwd_rla00pctprof, c("cwd_lower", "cwd_upper"), sep = "-") %>% 
  mutate(
    cwd_upper = ifelse(is.na(cwd_upper), cwd_lower, cwd_upper),
    cwd_lower = as.numeric(cwd_lower),
    cwd_upper = as.numeric(cwd_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_cwd = mean(c(cwd_lower, cwd_upper))) %>% 
  ungroup()

# ecd = economically disadvantaged
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(ecd_rla00pctprof, c("ecd_lower", "ecd_upper"), sep = "-") %>% 
  mutate(
    ecd_upper = ifelse(is.na(ecd_upper), ecd_lower, ecd_upper),
    ecd_lower = as.numeric(ecd_lower),
    ecd_upper = as.numeric(ecd_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_ecd = mean(c(ecd_lower, ecd_upper))) %>% 
  ungroup()

# lep = limited English proficiency (English Language Learner)
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(lep_rla00pctprof, c("lep_lower", "lep_upper"), sep = "-") %>% 
  mutate(
    lep_upper = ifelse(is.na(lep_upper), lep_lower, lep_upper),
    lep_lower = as.numeric(lep_lower),
    lep_upper = as.numeric(lep_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_lep = mean(c(lep_lower, lep_upper))) %>% 
  ungroup()

# Get ride of the "_lower" and "_upper" percentage columns since they won't be needed
viz3_rlalea00 <- viz3_rlalea00 %>% 
  select(year, 
         stnam, 
         fipst,
         leaid,
         contains("meanpctprof"))
# Pivoted the dataset longer to have a column for subgroup and a column for mean percentage proficient. 

viz3_rlalea00_long <- viz3_rlalea00 %>%
  pivot_longer(
        cols = contains("meanpctprof"),
        names_to = "subgroup",
        values_to = "meanpctprof",
        names_prefix = "meanpctprof_") 
# Joined the long file with the cleaned/narrowed fiscal data file. Used an inner join because I'm only interested in LEAs that have both student proficiency and fiscal data. 
viz3_rla00long_fiscal_2010 <- inner_join(viz3_rlalea00_long, viz3_fiscal2010, by = "leaid")

Potential Visualizations

These are the data visualizations I am thinking I will choose from for our final product. I’m not going to include all of these. My primary next step is to narrow them down. Please note that they are still a bit rough and need refinement (some more than others). Aside from determining which visualizations to include, I plan to finalize color, try out annotations and/or highlighting, and explore alternate options for faceting by state. If I include the plots with fitted lines, I plan to update the colors and replace the legend with annotations.

First off, bar graphs. The first two summarize LEA local revenue and LEA local revenue from property taxes averaged by state. The third graph summarizes the average percentage of students scoring at/above proficient by state.

# Bar graph: Average LEA total local revenue ($ per student) by state
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "all")  %>% 
  group_by(stnam) %>% 
  summarize(mean_locrev_stu = mean(locrev_stu)) %>% 
  ggplot(aes(x = mean_locrev_stu, y = fct_reorder(stnam, mean_locrev_stu))) +
  geom_col(color = "white", alpha = .6) +
  scale_x_continuous(expand = c(0, 0),
                     breaks = c(0, 2500, 5000, 7500, 10000, 12500),
                     labels = scales::dollar) +
  labs(title = "Average Local Revenue of LEAs",
       y = "State",
       x = "Dollar per student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())  

# Bar graph: Average LEA local revenue from property taxes ($ per student) by state
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "all")  %>% 
  group_by(stnam) %>% 
  summarize(mean_locrevtaxes_stu = mean(locrevtaxes_stu)) %>% 
  ggplot(aes(x = mean_locrevtaxes_stu, y = fct_reorder(stnam, mean_locrevtaxes_stu))) +
  geom_col(color = "white", alpha = .6) +
  scale_x_continuous(expand = c(0, 0),
                    breaks = c(0, 2500, 5000, 7500, 10000),
                     labels = scales::dollar) +
  labs(title = "Average Local Revenue of LEAs from Property Taxes",
       y = "State",
       x = "Dollar per student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())

# Bar graph of average RLA proficiency for each state
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "all")  %>% 
  group_by(stnam) %>% 
  summarize(mean_pctprof = mean(meanpctprof, na.rm = T)) %>% 
  ggplot(aes(x = mean_pctprof, y = fct_reorder(stnam, mean_pctprof))) +
  geom_col(color = "white", alpha = .6) +
  scale_x_continuous(expand = c(0, 0),
                    breaks = c(0, 20, 40, 60, 80),
                    labels = c("0%", "20%", "40%", "60%", "80%")) +
  labs(title = "Average Proficiency in Reading/Language Arts",
       subtitle = "Students in Grades 3 through HS",
       y = "State",
       x = "Average Percentage",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())  

Density ridges of local revenue from property taxes for LEAs by state. One thing I plan to do, if I include this plot, that I didn’t get to is sorting the states by mean revenue.

# Density ridges of local revenue from property taxes across LEAs by state
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "all")  %>% 
  ggplot(aes(x = locrevtaxes_stu, y = stnam)) +
    geom_density_ridges(fill = "cornflower blue", color = "white", alpha = .8) +
    theme_minimal() +
  labs(title = "Local Revenue from Property Taxes in LEAS of Each State",
       y = "State",
       x = "Dollar per student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot") + 
  scale_x_continuous(expand = c(0, 0)) +
  coord_cartesian(xlim = c(0, 25000))

Scatterplots showing the relationship between types of revenue and average percentage of students scoring at/above proficient on statewide assessments of reading/language arts. Note that I used a log transformation of the x-axis to spread the points out. Without the transformation, the bulk of the points were clustered near the bottom of the range. I think that I could use highlighting and annotations for the lowest and highest points.

# Scatterplots: All students, LEA total local revenue ($ per stu) and LEA local revenue from property taxes by approx. pct proficient
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "all") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  geom_point(color = "gray30", fill = "gray30", alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Relationship between Local Revenue and RLA Proficiency",
       y = "Approximate Average Percent Proficient",
       x = "Dollar per Student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank()) 

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "all") %>% 
  ggplot(aes(x = locrevtaxes_stu, y = meanpctprof)) +
  geom_point(color = "gray30", fill = "gray30", alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Relationship between Local Revenue from Property Taxes and RLA Proficiency",
       y = "Approximate Average Percent Proficient",
       x = "Dollar per Student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())

Scatterplots faceted by state.

# Scatterplot: All students - relationship between local revenue ($ per student) and mean % proficient, faceted by state
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "all") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(color = "gray30", fill = "gray30", alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Relationship between Total Local Revenue and RLA Proficiency",
       y = "Approximate Average Percent Proficient",
       x = "Dollar per Student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank()) 

# Scatterplot: All students - relationship between local revenue from property taxes ($ per student) and mean % proficient, faceted by state
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "all") %>% 
  ggplot(aes(x = locrevtaxes_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(color = "gray30", fill = "gray30", alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Relationship between Local Revenue from Property Taxes and RLA Proficiency",
       y = "Approximate Average Percent Proficient",
       x = "Dollar per Student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())

Scatterplots showing the relationship between revenue and outcomes, faceted by subgroup. These are still pretty rough (e.g., labels need work).

# Scatterplot: Relationship between local revenue ($ per student) and mean % proficient, faceted by subgroup
viz3_rla00long_fiscal_2010 %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~subgroup) +
  geom_point(color = "gray30", fill = "gray30", alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Relationship between Total Local Revenue and RLA Proficiency",
       y = "Approximate Average Percent Proficient",
       x = "Dollar per Student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank()) 

# Scatterplot: Relationship between local revenue from property taxes ($ per student) and mean % proficient, faceted by subgroup
viz3_rla00long_fiscal_2010 %>% 
  ggplot(aes(x = locrevtaxes_stu, y = meanpctprof)) +
  facet_wrap(~subgroup) +
  geom_point(color = "gray30", fill = "gray30", alpha = .4) +
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Relationship between Local Revenue from Property Taxes and RLA Proficiency",
       y = "Approximate Average Percent Proficient",
       x = "Dollar per Student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())

Playing around with fitting lines. These are still rough. I need to work on the legend and want to try replacing it with annotations. Also could consider changing the color to highlight a specific group or pick a different color palette.

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup != "all") %>% 
  ggplot() +
  geom_smooth(method = lm, se = F, aes(x = locrevtaxes_stu, y = meanpctprof, color = subgroup)) +
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Relationship between Local Revenue from Property Taxes and RLA Proficiency",
       y = "Approximate Average Percent Proficient",
       x = "Dollar per Student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())

Fitted lines faceted by state (also very rough).

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup != "all") %>% 
  ggplot() +
  geom_smooth(method = lm, se = F, aes(x = locrevtaxes_stu, y = meanpctprof, color = subgroup)) +
  facet_wrap(~stnam) +
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Relationship between Local Revenue from Property Taxes and RLA Proficiency",
       y = "Approximate Average Percent Proficient",
       x = "Dollar per Student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())

Created maps displaying (a) average LEA local revenue in each state and (b) average LEA local revenue from property taxes in each state. I still need to fill in the missing states and want to try out different color palettes

viz3_us <- usa_sf() 

viz3_map <- left_join(viz3_rla00long_fiscal_2010, 
                      viz3_us, 
                      by = c("stnam" = "name"))

viz3_map %>% 
  filter(subgroup == "all") %>% 
  group_by(stnam) %>% 
  mutate(mean_locrev_stu = mean(locrev_stu)) %>% 
  ggplot(aes(geometry = geometry, fill = mean_locrev_stu)) +
  geom_sf(color = "white", size = 0) +
    scale_fill_viridis(option = "magma", 
                       name = "Dollar per student",
                                         breaks = c(0, 2500, 5000, 7500, 10000, 12500),
                                         labels = c("$0", 
                                                    "$2,500", 
                                                    "$5,000", 
                                                    "$7,500", 
                                                    "$10,000",
                                                    "$12,5000")) +
  theme_void() +
  labs(title = "Average LEA Total Local Revenue",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme(plot.title.position = "plot") 

viz3_map %>% 
  filter(subgroup == "all") %>% 
  group_by(stnam) %>% 
  mutate(mean_locrevtaxes_stu = mean(locrevtaxes_stu)) %>% 
  ggplot(aes(geometry = geometry, fill = mean_locrevtaxes_stu)) +
  geom_sf(color = "white", size = 0) +
    scale_fill_viridis(option = "magma", 
                       name = "Dollar per student",
                                         breaks = c(0, 2500, 5000, 7500, 10000),
                                         labels = c("$0", 
                                                    "$2,500", 
                                                    "$5,000", 
                                                    "$7,500", 
                                                    "$10,000")) +
  theme_void() +
  labs(title = "Average LEA Local Revenue from Property Taxes",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme(plot.title.position = "plot") 

Other Data Visualizations

These are some of the preliminary visualizations I did that I don’t think I’m moving forward with.

# Scatterplot: Total local LEA revenue from property taxes ($ per stu) x approx. pct proficient, color = subgroup
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup != "all") %>% 
  ggplot(aes(x = locrevtaxes_stu, y = meanpctprof, color = subgroup)) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  theme_minimal() +
  labs(title = "Revenue from property tax x meanpctprof, color by subgroup") +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())

Fitted lines for specific states:

# fitted lines in a few specific states
viz3_rla00long_fiscal_2010 %>% 
  filter(stnam == "Montana") %>% 
  ggplot() +
  geom_smooth(method = lm, se = F, aes(x = locrevtaxes_stu, y = meanpctprof, color = subgroup)) +
  scale_x_log10(labels = scales::dollar) +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())  +
  labs(title = "Montana")

viz3_rla00long_fiscal_2010 %>% 
  filter(stnam == "South Dakota") %>% 
  ggplot() +
  geom_smooth(method = lm, se = F, aes(x = locrevtaxes_stu, y = meanpctprof, color = subgroup)) +
  scale_x_log10(labels = scales::dollar) +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank()) +
  labs(title = "South Dakota")

viz3_rla00long_fiscal_2010 %>% 
  filter(stnam == "New Jersey") %>% 
  ggplot() +
  geom_smooth(method = lm, se = F, aes(x = locrevtaxes_stu, y = meanpctprof, color = subgroup)) +
  scale_x_log10(labels = scales::dollar) +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank()) +
  labs(title = "New Jersey")

# Fitting a line over data points for local revenue x meanpctprof by state
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "all") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  geom_smooth(method = "lm") +
  facet_wrap(~stnam) +
  geom_point(alpha = .1) + 
  scale_x_log10(labels = scales::dollar) 

# Scatterplot: Relationship between local revenue from property taxes ($ per student) and mean % proficient, faceted by state, color by subgroup 
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup != "all") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof, color = subgroup)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar)

Scatterplots for each student subgroup between local revenue from property tax and % proficient, faceted by state. The subgroup is indicated in the title.

# Scatterplots: Relationship between local revenue from property taxes ($ per student) and mean % proficient, faceted by state for each subgroup 

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "mam") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "American Indian/Alaska Native")

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "mas") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Asian/Pacific Islander")

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "mhi") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Hispanic/Latino")

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "mbl") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Black")

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "mwh") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "White")

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "mtr") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Multiracial")

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "cwd") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Students with Disabilities")

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "ecd") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Economically Disadvantaged")

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "lep") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Limited English Proficiency")